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Wagner SK, Patel PJ, Huemer J, Khalid H, Stuart KV, Chu CJ, Williamson DJ, Struyven RR, Romero-Bascones D, Foster PJ, Khawaja AP, Petzold A, Balaskas K, Cortina-Borja M, Chapple I, Dietrich T, Rahi JS, Denniston AK, Keane PA. Periodontitis and Outer Retinal Thickness: a Cross-Sectional Analysis of the United Kingdom Biobank Cohort. Ophthalmol Sci 2024; 4:100472. [PMID: 38560277 PMCID: PMC10973663 DOI: 10.1016/j.xops.2024.100472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 10/31/2023] [Accepted: 01/12/2024] [Indexed: 04/04/2024]
Abstract
Purpose Periodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone, can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration (AMD), in individuals with no eye disease. Design Cross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank. Participants Sixty-seven thousand three hundred eleven UK residents aged 40 to 70 years recruited between 2006 and 2010 underwent retinal imaging. Methods Macular-centered OCT images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses, adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery. Main Outcome Measures Photoreceptor layer (PRL) and retinal pigment epithelium-Bruch's membrane (RPE-BM) thicknesses. Results Among 36 897 participants included in the analysis, 1571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation, and were more likely to be hypertensive, diabetic, and current smokers (all P < 0.001). On average, those with very severe periodontitis were hyperopic (0.05 ± 2.27 diopters) while those unaffected were myopic (-0.29 ± 2.40 diopters, P < 0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 μm, 95% confidence interval [CI], -0.97 to -0.12; P = 0.022) but there was no difference in RPE-BM thickness (0.00 μm, 95% CI, -0.12 to 0.13; P = 0.97). The association between PRL thickness and very severe periodontitis was modified by age (P < 0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60 to 69 years with disease (-1.19 μm, 95% CI, -1.85 to -0.53; P < 0.001) but not among those aged < 60 years. Conclusions Among those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient AMD. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Siegfried K. Wagner
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Praveen J. Patel
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Josef Huemer
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- Department of Ophthalmology and Optometry, Kepler University Hospital, Linz, Austria
| | - Hagar Khalid
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Kelsey V. Stuart
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Colin J. Chu
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Dominic J. Williamson
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, United Kingdom
| | - Robbert R. Struyven
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, United Kingdom
| | - David Romero-Bascones
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Mondragón, Spain
| | - Paul J. Foster
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Anthony P. Khawaja
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Axel Petzold
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Konstantinos Balaskas
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Mario Cortina-Borja
- Population, Policy and Practice, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Iain Chapple
- NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- School of Dentistry, Birmingham Community Healthcare NHS Foundation Trust, United Kingdom
| | - Thomas Dietrich
- NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- School of Dentistry, Birmingham Community Healthcare NHS Foundation Trust, United Kingdom
| | - Jugnoo S. Rahi
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- Population, Policy and Practice, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at UCL Great Ormond Street Institute of Child Health and Great Ormond Street Hospital, London, United Kingdom
- Department of Ophthalmology, Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
- Ulverscroft Vision Research Group, Institute of Child Health, University College London, London, United Kingdom
| | - Alastair K. Denniston
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
- NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Pearse A. Keane
- Population and Data Sciences, Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
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Bowness JS, Liu X, Keane PA. Leading in the development, standardised evaluation, and adoption of artificial intelligence in clinical practice: regional anaesthesia as an example. Br J Anaesth 2024; 132:1016-1021. [PMID: 38302346 DOI: 10.1016/j.bja.2023.12.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/19/2023] [Accepted: 12/27/2023] [Indexed: 02/03/2024] Open
Abstract
A recent study by Suissa and colleagues explored the clinical relevance of a medical image segmentation metric (Dice metric) commonly used in the field of artificial intelligence (AI). They showed that pixel-wise agreement for physician identification of structures on ultrasound images is variable, and a relatively low Dice metric (0.34) correlated to a substantial agreement on subjective clinical assessment. We highlight the need to bring structure and clinical perspective to the evaluation of medical AI, which clinicians are best placed to direct.
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Affiliation(s)
- James S Bowness
- Nuffield Department of Clinical Anaesthesia, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Pearse A Keane
- Institute of Ophthalmology, Faculty of Brain Sciences, University College London, London, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Shi D, Zhou Y, He S, Wagner SK, Huang Y, Keane PA, Ting DS, Zhang L, Zheng Y, He M. Cross-modality Labeling Enables Noninvasive Capillary Quantification as a Sensitive Biomarker for Assessing Cardiovascular Risk. Ophthalmol Sci 2024; 4:100441. [PMID: 38420613 PMCID: PMC10899028 DOI: 10.1016/j.xops.2023.100441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 11/26/2023] [Accepted: 11/27/2023] [Indexed: 03/02/2024]
Abstract
Purpose We aim to use fundus fluorescein angiography (FFA) to label the capillaries on color fundus (CF) photographs and train a deep learning model to quantify retinal capillaries noninvasively from CF and apply it to cardiovascular disease (CVD) risk assessment. Design Cross-sectional and longitudinal study. Participants A total of 90732 pairs of CF-FFA images from 3893 participants for segmentation model development, and 49229 participants in the UK Biobank for association analysis. Methods We matched the vessels extracted from FFA and CF, and used vessels from FFA as labels to train a deep learning model (RMHAS-FA) to segment retinal capillaries using CF. We tested the model's accuracy on a manually labeled internal test set (FundusCapi). For external validation, we tested the segmentation model on 7 vessel segmentation datasets, and investigated the clinical value of the segmented vessels in predicting CVD events in the UK Biobank. Main Outcome Measures Area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity for segmentation. Hazard ratio (HR; 95% confidence interval [CI]) for Cox regression analysis. Results On the FundusCapi dataset, the segmentation performance was AUC = 0.95, accuracy = 0.94, sensitivity = 0.90, and specificity = 0.93. Smaller vessel skeleton density had a stronger correlation with CVD risk factors and incidence (P < 0.01). Reduced density of small vessel skeletons was strongly associated with an increased risk of CVD incidence and mortality for women (HR [95% CI] = 0.91 [0.84-0.98] and 0.68 [0.54-0.86], respectively). Conclusions Using paired CF-FFA images, we automated the laborious manual labeling process and enabled noninvasive capillary quantification from CF, supporting its potential as a sensitive screening method for identifying individuals at high risk of future CVD events. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yukun Zhou
- Centre for Medical Image Computing, University College London, London, UK
| | - Shuang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Siegfried K. Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Yu Huang
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Pearse A. Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Daniel S.W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, and Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Lei Zhang
- Faculty of Medicine, Central Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mingguang He
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
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Zhou Y, Xu M, Hu Y, Blumberg SB, Zhao A, Wagner SK, Keane PA, Alexander DC. CF-Loss: Clinically-relevant feature optimised loss function for retinal multi-class vessel segmentation and vascular feature measurement. Med Image Anal 2024; 93:103098. [PMID: 38320370 DOI: 10.1016/j.media.2024.103098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 05/22/2023] [Accepted: 01/30/2024] [Indexed: 02/08/2024]
Abstract
Characterising clinically-relevant vascular features, such as vessel density and fractal dimension, can benefit biomarker discovery and disease diagnosis for both ophthalmic and systemic diseases. In this work, we explicitly encode vascular features into an end-to-end loss function for multi-class vessel segmentation, categorising pixels into artery, vein, uncertain pixels, and background. This clinically-relevant feature optimised loss function (CF-Loss) regulates networks to segment accurate multi-class vessel maps that produce precise vascular features. Our experiments first verify that CF-Loss significantly improves both multi-class vessel segmentation and vascular feature estimation, with two standard segmentation networks, on three publicly available datasets. We reveal that pixel-based segmentation performance is not always positively correlated with accuracy of vascular features, thus highlighting the importance of optimising vascular features directly via CF-Loss. Finally, we show that improved vascular features from CF-Loss, as biomarkers, can yield quantitative improvements in the prediction of ischaemic stroke, a real-world clinical downstream task. The code is available at https://github.com/rmaphoh/feature-loss.
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Affiliation(s)
- Yukun Zhou
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 9EL, UK; Institute of Ophthalmology, University College London, London EC1V 9EL, UK.
| | - MouCheng Xu
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK
| | - Yipeng Hu
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Medical Physics and Biomedical Engineering, University College London, London WC1E 6BT, UK; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London W1W 7TS, UK
| | - Stefano B Blumberg
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Computer Science, University College London, London WC1E 6BT, UK
| | - An Zhao
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Siegfried K Wagner
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 9EL, UK; Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London EC1V 9EL, UK; Institute of Ophthalmology, University College London, London EC1V 9EL, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK; Department of Computer Science, University College London, London WC1E 6BT, UK
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Carmichael J, Costanza E, Blandford A, Struyven R, Keane PA, Balaskas K. Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs. Sci Rep 2024; 14:6775. [PMID: 38514657 PMCID: PMC10958016 DOI: 10.1038/s41598-024-55410-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 02/23/2024] [Indexed: 03/23/2024] Open
Abstract
Artificial intelligence (AI) has great potential in ophthalmology. We investigated how ambiguous outputs from an AI diagnostic support system (AI-DSS) affected diagnostic responses from optometrists when assessing cases of suspected retinal disease. Thirty optometrists (15 more experienced, 15 less) assessed 30 clinical cases. For ten, participants saw an optical coherence tomography (OCT) scan, basic clinical information and retinal photography ('no AI'). For another ten, they were also given AI-generated OCT-based probabilistic diagnoses ('AI diagnosis'); and for ten, both AI-diagnosis and AI-generated OCT segmentations ('AI diagnosis + segmentation') were provided. Cases were matched across the three types of presentation and were selected to include 40% ambiguous and 20% incorrect AI outputs. Optometrist diagnostic agreement with the predefined reference standard was lowest for 'AI diagnosis + segmentation' (204/300, 68%) compared to 'AI diagnosis' (224/300, 75% p = 0.010), and 'no Al' (242/300, 81%, p = < 0.001). Agreement with AI diagnosis consistent with the reference standard decreased (174/210 vs 199/210, p = 0.003), but participants trusted the AI more (p = 0.029) with segmentations. Practitioner experience did not affect diagnostic responses (p = 0.24). More experienced participants were more confident (p = 0.012) and trusted the AI less (p = 0.038). Our findings also highlight issues around reference standard definition.
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Affiliation(s)
- Josie Carmichael
- University College London Interaction Centre (UCLIC), UCL, London, UK.
- Institute of Ophthalmology, NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, London, UK.
| | - Enrico Costanza
- University College London Interaction Centre (UCLIC), UCL, London, UK
| | - Ann Blandford
- University College London Interaction Centre (UCLIC), UCL, London, UK
| | - Robbert Struyven
- Institute of Ophthalmology, NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, London, UK
| | - Pearse A Keane
- Institute of Ophthalmology, NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, London, UK
| | - Konstantinos Balaskas
- Institute of Ophthalmology, NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL, London, UK
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Fu DJ, Glinton S, Lipkova V, Faes L, Liefers B, Zhang G, Pontikos N, McKeown A, Scheibler L, Patel PJ, Keane PA, Balaskas K. Deep-learning automated quantification of longitudinal OCT scans demonstrates reduced RPE loss rate, preservation of intact macular area and predictive value of isolated photoreceptor degeneration in geographic atrophy patients receiving C3 inhibition treatment. Br J Ophthalmol 2024; 108:536-545. [PMID: 37094835 PMCID: PMC10958254 DOI: 10.1136/bjo-2022-322672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 03/15/2023] [Indexed: 04/26/2023]
Abstract
OBJECTIVE To evaluate the role of automated optical coherence tomography (OCT) segmentation, using a validated deep-learning model, for assessing the effect of C3 inhibition on the area of geographic atrophy (GA); the constituent features of GA on OCT (photoreceptor degeneration (PRD), retinal pigment epithelium (RPE) loss and hypertransmission); and the area of unaffected healthy macula.To identify OCT predictive biomarkers for GA growth. METHODS Post hoc analysis of the FILLY trial using a deep-learning model for spectral domain OCT (SD-OCT) autosegmentation. 246 patients were randomised 1:1:1 into pegcetacoplan monthly (PM), pegcetacoplan every other month (PEOM) and sham treatment (pooled) for 12 months of treatment and 6 months of therapy-free monitoring. Only participants with Heidelberg SD-OCT were included (n=197, single eye per participant).The primary efficacy endpoint was the square root transformed change in area of GA as complete RPE and outer retinal atrophy (cRORA) in each treatment arm at 12 months, with secondary endpoints including RPE loss, hypertransmission, PRD and intact macular area. RESULTS Eyes treated PM showed significantly slower mean change of cRORA progression at 12 and 18 months (0.151 and 0.277 mm, p=0.0039; 0.251 and 0.396 mm, p=0.039, respectively) and RPE loss (0.147 and 0.287 mm, p=0.0008; 0.242 and 0.410 mm, p=0.00809). PEOM showed significantly slower mean change of RPE loss compared with sham at 12 months (p=0.0313). Intact macular areas were preserved in PM compared with sham at 12 and 18 months (p=0.0095 and p=0.044). PRD in isolation and intact macula areas was predictive of reduced cRORA growth at 12 months (coefficient 0.0195, p=0.01 and 0.00752, p=0.02, respectively) CONCLUSION: The OCT evidence suggests that pegcetacoplan slows progression of cRORA overall and RPE loss specifically while protecting the remaining photoreceptors and slowing the progression of healthy retina to iRORA.
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Affiliation(s)
- Dun Jack Fu
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Sophie Glinton
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Veronika Lipkova
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Livia Faes
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Bart Liefers
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Gongyu Zhang
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Nikolas Pontikos
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Alex McKeown
- Apellis Pharmaceuticals Inc, Waltham, Massachusetts, USA
| | | | - Praveen J Patel
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & UCL, Institute of Ophthalmology, London, UK
- University College London, London, UK
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Wagner SK, Raja L, Cortina-Borja M, Huemer J, Struyven R, Keane PA, Balaskas K, Sim DA, Thomas PBM, Rahi JS, Solebo AL, Kang S. Determinants of non-attendance at face-to-face and telemedicine ophthalmic consultations. Br J Ophthalmol 2024; 108:625-632. [PMID: 37217292 PMCID: PMC10958256 DOI: 10.1136/bjo-2022-322389] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 04/05/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND/AIMS Evaluation of telemedicine care models has highlighted its potential for exacerbating healthcare inequalities. This study seeks to identify and characterise factors associated with non-attendance across face-to-face and telemedicine outpatient appointments. METHODS A retrospective cohort study at a tertiary-level ophthalmic institution in the UK, between 1 January 2019 and 31 October 2021. Logistic regression modelled non-attendance against sociodemographic, clinical and operational exposure variables for all new patient registrations across five delivery modes: asynchronous, synchronous telephone, synchronous audiovisual and face to face prior to the pandemic and face to face during the pandemic. RESULTS A total of 85 924 patients (median age 55 years, 54.4% female) were newly registered. Non-attendance differed significantly by delivery mode: (9.0% face to face prepandemic, 10.5% face to face during the pandemic, 11.7% asynchronous and 7.8%, synchronous during pandemic). Male sex, greater levels of deprivation, a previously cancelled appointment and not self-reporting ethnicity were strongly associated with non-attendance across all delivery modes. Individuals identifying as black ethnicity had worse attendance in synchronous audiovisual clinics (adjusted OR 4.24, 95% CI 1.59 to 11.28) but not asynchronous. Those not self-reporting their ethnicity were from more deprived backgrounds, had worse broadband access and had significantly higher non-attendance across all modes (all p<0.001). CONCLUSION Persistent non-attendance among underserved populations attending telemedicine appointments highlights the challenge digital transformation faces for reducing healthcare inequalities. Implementation of new programmes should be accompanied by investigation into the differential health outcomes of vulnerable populations.
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Affiliation(s)
- Siegfried K Wagner
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Laxmi Raja
- Digital Clinical Laboratory, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Mario Cortina-Borja
- Great Ormond Street Institute of Child Health, University College London, London, UK
| | - Josef Huemer
- Department of Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Robbert Struyven
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Centre for Medical Image Computing, University College London, London, UK
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Konstantinos Balaskas
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Dawn A Sim
- Institute of Ophthalmology, University College London, London, UK
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL, London, UK
| | - Peter B M Thomas
- Institute of Ophthalmology, University College London, London, UK
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL, London, UK
| | - Jugnoo S Rahi
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Department of Ophthamology, Great Ormond Street Hospital NHS Foundation Trust, London, UK
- Ulverscroft Vision Research Group, University College London, London, UK
| | - Ameenat Lola Solebo
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Great Ormond Street Institute of Child Health, University College London, London, UK
- Department of Ophthamology, Great Ormond Street Hospital NHS Foundation Trust, London, UK
- Ulverscroft Vision Research Group, University College London, London, UK
| | - Swan Kang
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Adnexal department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Ong AY, Hogg HDJ, Kale AU, Taribagil P, Kras A, Dow E, Macdonald T, Liu X, Keane PA, Denniston AK. AI as a Medical Device for Ophthalmic Imaging in Europe, Australia, and the United States: Protocol for a Systematic Scoping Review of Regulated Devices. JMIR Res Protoc 2024; 13:e52602. [PMID: 38483456 PMCID: PMC10979335 DOI: 10.2196/52602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 02/10/2024] [Accepted: 02/20/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Artificial intelligence as a medical device (AIaMD) has the potential to transform many aspects of ophthalmic care, such as improving accuracy and speed of diagnosis, addressing capacity issues in high-volume areas such as screening, and detecting novel biomarkers of systemic disease in the eye (oculomics). In order to ensure that such tools are safe for the target population and achieve their intended purpose, it is important that these AIaMD have adequate clinical evaluation to support any regulatory decision. Currently, the evidential requirements for regulatory approval are less clear for AIaMD compared to more established interventions such as drugs or medical devices. There is therefore value in understanding the level of evidence that underpins AIaMD currently on the market, as a step toward identifying what the best practices might be in this area. In this systematic scoping review, we will focus on AIaMD that contributes to clinical decision-making (relating to screening, diagnosis, prognosis, and treatment) in the context of ophthalmic imaging. OBJECTIVE This study aims to identify regulator-approved AIaMD for ophthalmic imaging in Europe, Australia, and the United States; report the characteristics of these devices and their regulatory approvals; and report the available evidence underpinning these AIaMD. METHODS The Food and Drug Administration (United States), the Australian Register of Therapeutic Goods (Australia), the Medicines and Healthcare products Regulatory Agency (United Kingdom), and the European Database on Medical Devices (European Union) regulatory databases will be searched for ophthalmic imaging AIaMD through a snowballing approach. PubMed and clinical trial registries will be systematically searched, and manufacturers will be directly contacted for studies investigating the effectiveness of eligible AIaMD. Preliminary regulatory database searches, evidence searches, screening, data extraction, and methodological quality assessment will be undertaken by 2 independent review authors and arbitrated by a third at each stage of the process. RESULTS Preliminary searches were conducted in February 2023. Data extraction, data synthesis, and assessment of methodological quality commenced in October 2023. The review is on track to be completed and submitted for peer review by April 2024. CONCLUSIONS This systematic review will provide greater clarity on ophthalmic imaging AIaMD that have achieved regulatory approval as well as the evidence that underpins them. This should help adopters understand the range of tools available and whether they can be safely incorporated into their clinical workflow, and it should also support developers in navigating regulatory approval more efficiently. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/52602.
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Affiliation(s)
- Ariel Yuhan Ong
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Henry David Jeffry Hogg
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
| | - Aditya U Kale
- Department of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
| | - Priyal Taribagil
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | | | - Eliot Dow
- Retinal Consultants Medical Group, Sacramento, CA, United States
| | - Trystan Macdonald
- Department of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre, Birmingham, United Kingdom
| | - Xiaoxuan Liu
- Department of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre, Birmingham, United Kingdom
- Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, United Kingdom
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
| | - Alastair K Denniston
- Department of Ophthalmology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre, Birmingham, United Kingdom
- Centre for Regulatory Science and Innovation, Birmingham Health Partners, Birmingham, United Kingdom
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9
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Martindale APL, Ng B, Ngai V, Kale AU, Ferrante di Ruffano L, Golub RM, Collins GS, Moher D, McCradden MD, Oakden-Rayner L, Rivera SC, Calvert M, Kelly CJ, Lee CS, Yau C, Chan AW, Keane PA, Beam AL, Denniston AK, Liu X. Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines. Nat Commun 2024; 15:1619. [PMID: 38388497 PMCID: PMC10883966 DOI: 10.1038/s41467-024-45355-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.
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Affiliation(s)
| | - Benjamin Ng
- Birmingham and Midland Eye Centre, Sandwell and West Birmingham NHS Trust, Birmingham, UK
- Christ Church, University of Oxford, Oxford, UK
| | - Victoria Ngai
- University College London Medical School, London, UK
| | - Aditya U Kale
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
| | | | - Robert M Golub
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Gary S Collins
- Centre for Statistics in Medicine//UK EQUATOR Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - David Moher
- Centre for Journalology, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottowa, ON, Canada
| | - Melissa D McCradden
- Department of Bioethics, The Hospital for Sick Children, Toronto, ON, Canada
- Genetics & Genome Biology Research Program, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada
- Division of Clinical and Public Health, Dalla Lana School of Public Health, Toronto, ON, Canada
| | - Lauren Oakden-Rayner
- Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia
| | - Samantha Cruz Rivera
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Melanie Calvert
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- Centre for Patient Reported Outcomes Research (CPROR), Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
- NIHR Applied Research Collaboration (ARC) West Midlands, University of Birmingham, Birmingham, UK
- NIHR Blood and Transplant Research Unit (BTRU) in Precision Transplant and Cellular Therapeutics, University of Birmingham, Birmingham, UK
| | | | | | - Christopher Yau
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Health Data Research UK, London, UK
| | - An-Wen Chan
- Department of Medicine, Women's College Hospital. University of Toronto, Toronto, ON, Canada
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Andrew L Beam
- Department of Epidemiology, Harvard. T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Alastair K Denniston
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
- National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK
- NIHR Biomedical Research Centre at Moorfields, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK.
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.
- Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK.
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10
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Gonçalves MB, Nakayama LF, Ferraz D, Faber H, Korot E, Malerbi FK, Regatieri CV, Maia M, Celi LA, Keane PA, Belfort R. Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review. Eye (Lond) 2024; 38:426-433. [PMID: 37667028 PMCID: PMC10858054 DOI: 10.1038/s41433-023-02717-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/26/2023] [Accepted: 08/25/2023] [Indexed: 09/06/2023] Open
Abstract
This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it's recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.
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Affiliation(s)
- Mariana Batista Gonçalves
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Luis Filipe Nakayama
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil.
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA.
| | - Daniel Ferraz
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Hanna Faber
- Department of Ophthalmology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
- Department of Ophthalmology, University of Tuebingen, Tuebingen, Germany
| | - Edward Korot
- Retina Specialists of Michigan, Grand Rapids, MI, USA
- Stanford University Byers Eye Institute Palo Alto, Palo Alto, CA, USA
| | | | | | - Mauricio Maia
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
| | - Leo Anthony Celi
- Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA, USA
- Harvard TH Chan School of Public Health, Department of Biostatistics, Boston, MA, USA
- Beth Israel Deaconess Medical Center, Department of Medicine, Boston, MA, USA
| | - Pearse A Keane
- NIHR Biomedical Research Centre for Ophthalmology, Moorfield Eye Hospital, NHS Foundation Trust, and UCL Institute of Ophthalmology, London, UK
| | - Rubens Belfort
- Department of Ophthalmology, Sao Paulo Federal University, São Paulo, SP, Brazil
- Instituto Paulista de Estudos e Pesquisas em Oftalmologia, IPEPO, Vision Institute, São Paulo, SP, Brazil
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11
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Maloca PM, Pfau M, Janeschitz-Kriegl L, Reich M, Goerdt L, Holz FG, Müller PL, Valmaggia P, Fasler K, Keane PA, Zarranz-Ventura J, Zweifel S, Wiesendanger J, Kaiser P, Enz TJ, Rothenbuehler SP, Hasler PW, Juedes M, Freichel C, Egan C, Tufail A, Scholl HPN, Denk N. Human selection bias drives the linear nature of the more ground truth effect in explainable deep learning optical coherence tomography image segmentation. J Biophotonics 2024; 17:e202300274. [PMID: 37795556 DOI: 10.1002/jbio.202300274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 09/11/2023] [Accepted: 10/04/2023] [Indexed: 10/06/2023]
Abstract
Supervised deep learning (DL) algorithms are highly dependent on training data for which human graders are assigned, for example, for optical coherence tomography (OCT) image annotation. Despite the tremendous success of DL, due to human judgment, these ground truth labels can be inaccurate and/or ambiguous and cause a human selection bias. We therefore investigated the impact of the size of the ground truth and variable numbers of graders on the predictive performance of the same DL architecture and repeated each experiment three times. The largest training dataset delivered a prediction performance close to that of human experts. All DL systems utilized were highly consistent. Nevertheless, the DL under-performers could not achieve any further autonomous improvement even after repeated training. Furthermore, a quantifiable linear relationship between ground truth ambiguity and the beneficial effect of having a larger amount of ground truth data was detected and marked as the more-ground-truth effect.
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Affiliation(s)
- Peter M Maloca
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Maximilian Pfau
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Lucas Janeschitz-Kriegl
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
| | - Michael Reich
- Eye Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lukas Goerdt
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Frank G Holz
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Philipp L Müller
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Ophthalmology, University of Bonn, Bonn, Germany
- Makula Center, Suedblick Eye Centers, Augsburg, Germany
| | - Philippe Valmaggia
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Katrin Fasler
- Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | - Sandrine Zweifel
- Department of Ophthalmology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | | | - Tim J Enz
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
| | | | - Pascal W Hasler
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
| | - Marlene Juedes
- Pharma Research and Early Development (pRED), Pharmaceutical Sciences (PS), Roche, Innovation Center Basel, Basel, Switzerland
| | - Christian Freichel
- Pharma Research and Early Development (pRED), Pharmaceutical Sciences (PS), Roche, Innovation Center Basel, Basel, Switzerland
| | - Catherine Egan
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Adnan Tufail
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Hendrik P N Scholl
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
| | - Nora Denk
- Institute of Molecular and Clinical Ophthalmology Basel (IOB), Basel, Switzerland
- Department of Ophthalmology, University Hospital Basel, Basel, Switzerland
- Pharma Research and Early Development (pRED), Pharmaceutical Sciences (PS), Roche, Innovation Center Basel, Basel, Switzerland
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12
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Chia MA, Hersch F, Sayres R, Bavishi P, Tiwari R, Keane PA, Turner AW. Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians. Br J Ophthalmol 2024; 108:268-273. [PMID: 36746615 PMCID: PMC10850716 DOI: 10.1136/bjo-2022-322237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 12/31/2022] [Indexed: 02/08/2023]
Abstract
BACKGROUND/AIMS Deep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness. METHODS We performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR. The validation set consisted of 1682 consecutive, single-field, macula-centred retinal photographs from 864 patients with diabetes (mean age 54.9 years, 52.4% women) at an Indigenous primary care service in Perth, Australia. Three-person adjudication by a panel of specialists served as the reference standard. RESULTS For mtmDR detection, sensitivity of the DLS was superior to the retina specialist (98.0% (95% CI, 96.5 to 99.4) vs 87.1% (95% CI, 83.6 to 90.6), McNemar's test p<0.001) with a small reduction in specificity (95.1% (95% CI, 93.6 to 96.4) vs 97.0% (95% CI, 95.9 to 98.0), p=0.006). For vtDR, the DLS's sensitivity was again superior to the human grader (96.2% (95% CI, 93.4 to 98.6) vs 84.4% (95% CI, 79.7 to 89.2), p<0.001) with a slight drop in specificity (95.8% (95% CI, 94.6 to 96.9) vs 97.8% (95% CI, 96.9 to 98.6), p=0.002). For all-cause referable DR, there was a substantial increase in sensitivity (93.7% (95% CI, 91.8 to 95.5) vs 74.4% (95% CI, 71.1 to 77.5), p<0.001) and a smaller reduction in specificity (91.7% (95% CI, 90.0 to 93.3) vs 96.3% (95% CI, 95.2 to 97.4), p<0.001). CONCLUSION The DLS showed improved sensitivity and similar specificity compared with a retina specialist for DR detection. This demonstrates its potential to support DR screening among Indigenous Australians, an underserved population with a high burden of diabetic eye disease.
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Affiliation(s)
- Mark A Chia
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
| | | | | | | | | | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Angus W Turner
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Nedlands, Western Australia, Australia
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13
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Cleland CR, Bascaran C, Makupa W, Shilio B, Sandi FA, Philippin H, Marques AP, Egan C, Tufail A, Keane PA, Denniston AK, Macleod D, Burton MJ. Artificial intelligence-supported diabetic retinopathy screening in Tanzania: rationale and design of a randomised controlled trial. BMJ Open 2024; 14:e075055. [PMID: 38272554 PMCID: PMC10824006 DOI: 10.1136/bmjopen-2023-075055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
INTRODUCTION Globally, diabetic retinopathy (DR) is a major cause of blindness. Sub-Saharan Africa is projected to see the largest proportionate increase in the number of people living with diabetes over the next two decades. Screening for DR is recommended to prevent sight loss; however, in many low and middle-income countries, because of a lack of specialist eye care staff, current screening services for DR are not optimal. The use of artificial intelligence (AI) for DR screening, which automates the grading of retinal photographs and provides a point-of-screening result, offers an innovative potential solution to improve DR screening in Tanzania. METHODS AND ANALYSIS We will test the hypothesis that AI-supported DR screening increases the proportion of persons with true referable DR who attend the central ophthalmology clinic following referral after screening in a single-masked, parallel group, individually randomised controlled trial. Participants (2364) will be randomised (1:1 ratio) to either AI-supported or the standard of care DR screening pathway. Participants allocated to the AI-supported screening pathway will receive their result followed by point-of-screening counselling immediately after retinal image capture. Participants in the standard of care arm will receive their result and counselling by phone once the retinal images have been graded in the usual way (typically after 2-4 weeks). The primary outcome is the proportion of persons with true referable DR attending the central ophthalmology clinic within 8 weeks of screening. Secondary outcomes, by trial arm, include the proportion of persons attending the central ophthalmology clinic out of all those referred, sensitivity and specificity, number of false positive referrals, acceptability and fidelity of AI-supported screening. ETHICS AND DISSEMINATION The London School of Hygiene & Tropical Medicine, Kilimanjaro Christian Medical Centre and Tanzanian National Institute of Medical Research ethics committees have approved the trial. The results will be submitted to peer-reviewed journals for publication. TRIAL REGISTRATION NUMBER ISRCTN18317152.
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Affiliation(s)
- Charles R Cleland
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, Tanzania
| | - Covadonga Bascaran
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
| | - William Makupa
- Eye Department, Kilimanjaro Christian Medical Centre, Moshi, Tanzania
| | - Bernadetha Shilio
- Ministry of Health, Community Development, Gender, Elderly and Children, Dodoma, Tanzania
| | - Frank A Sandi
- Department of Ophthalmology, University of Dodoma School of Medicine and Nursing, Dodoma, Tanzania
| | - Heiko Philippin
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
- Eye Centre, University of Freiburg Faculty of Medicine, Freiburg, Germany
| | - Ana Patricia Marques
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
| | - Catherine Egan
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
| | - Adnan Tufail
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
| | - Alastair K Denniston
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
- National Institute for Health and Care Research, Birmingham Biomedical Research Centre, Birmingham, UK
| | - David Macleod
- Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Matthew J Burton
- International Centre for Eye Health, London School of Hygiene & Tropical Medicine, London, UK
- National Institute for Health and Care Research (NIHR) Biomedical Research Centre (BRC) for Ophthalmology, University College London, Moorfields Hospital London NHS Foundation Trust and Institute of Ophthalmology, London, UK
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14
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Hazell M, Reeves B, Rogers CA, Pike K, Culliford L, Baos S, Lui MPY, Beare NAV, Pavesio C, Denniston AK, Wordsworth S, Keane PA, Wilson R, Folkard A, Peto T, Sharma SM, Dick A. Adalimumab vs placebo as add-on to Standard Therapy for autoimmune Uveitis: Tolerability, Effectiveness and cost-effectiveness-a protocol for a randomised controlled trial (ASTUTE trial). BMJ Open 2024; 14:e082246. [PMID: 38267244 PMCID: PMC10824044 DOI: 10.1136/bmjopen-2023-082246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/21/2023] [Indexed: 01/26/2024] Open
Abstract
INTRODUCTION Adalimumab is an effective treatment for autoimmune non-infectious uveitis (ANIU), but it is currently only funded for a minority of patients with ANIU in the UK as it is restricted by the National Institute for Health and Care Excellence guidance. Ophthalmologists believe that adalimumab may be effective in a wider range of patients. The Adalimumab vs placebo as add-on to Standard Therapy for autoimmune Uveitis: Tolerability, Effectiveness and cost-effectiveness (ASTUTE) trial will recruit patients with ANIU who do and do not meet funding criteria and will evaluate the effectiveness and cost-effectiveness of adalimumab versus placebo as an add-on therapy to standard care. METHODS AND ANALYSIS The ASTUTE trial is a multicentre, parallel-group, placebo-controlled, pragmatic randomised controlled trial with a 16-week treatment run-in (TRI). At the end of the TRI, only responders will be randomised (1:1) to 40 mg adalimumab or placebo (both are the study investigational medicinal product) self-administered fortnightly by subcutaneous injection. The target sample size is 174 randomised participants. The primary outcome is time to treatment failure (TF), a composite of signs indicative of active ANIU. Secondary outcomes include individual TF components, retinal morphology, adverse events, health-related quality of life, patient-reported side effects and visual function, best-corrected visual acuity, employment status and resource use. In the event of TF, open-label drug treatment will be restarted as per TRI for 16 weeks, and if a participant responds again, allocation will be switched without unmasking and treatment with investigational medicinal product restarted. ETHICS AND DISSEMINATION The trial received Research Ethics Committee (REC) approval from South Central - Oxford B REC in June 2020. The findings will be presented at international meetings, by peer-reviewed publications and through patient organisations and newsletters to patients, where available. TRIAL REGISTRATION ISRCTN31474800. Registered 14 April 2020.
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Affiliation(s)
- Mae Hazell
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | | | - Chris A Rogers
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Katie Pike
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Lucy Culliford
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Sarah Baos
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | - Mandy P Y Lui
- Bristol Trials Centre, University of Bristol, Bristol, UK
| | | | - Carlos Pavesio
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | - Sarah Wordsworth
- University of Oxford, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | | | - Tunde Peto
- Centre for Public Health, NetwORC UK, Queen's University Belfast, Belfast, UK
| | - Srilakshmi M Sharma
- Oxford Eye Hospital, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- NIHR Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK
| | - Andrew Dick
- University of Bristol, Bristol, UK
- NIHR Biomedical Research Centre, Moorfields and UCL Institute of Ophthalmology, London, UK
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15
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Kaçar S, Coric D, Ometto G, Montesano G, Denniston AK, Keane PA, Uitdehaag BMJ, Crabb DP, Schoonheim MM, Petzold A, Strijbis EMM. Exploring Vitreous Haze as a Potential Biomarker for Accelerated Glymphatic Outflow and Neurodegeneration in Multiple Sclerosis: A Cross-Sectional Study. Brain Sci 2023; 14:36. [PMID: 38248251 PMCID: PMC10813039 DOI: 10.3390/brainsci14010036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/08/2023] [Accepted: 12/25/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND The glymphatic system removes neurodegenerative debris. The ocular glymphatic outflow is from the eye to the proximal optic nerve. In multiple sclerosis (MS), atrophy of the optic nerve increases the glymphatic outflow space. Here, we tested whether vitreous haze (VH) can provide novel insights into the relationship between neurodegeneration and the ocular glymphatic system in MS. METHODS This cross-sectional study comprised 315 persons with MS and 87 healthy controls (HCs). VH was quantified from optical coherence tomography (OCT) volume scans. Neurodegeneration was determined on three-dimensional T1 (3DT1) MRI, lesion detection on fluid-attenuated inversion (FLAIR), and layer thickness on OCT. Generalized estimating equations, corrected for age, were used to analyze associations between VH and metrics for neurodegeneration, demographics, and clinical scales. Group differences were determined between mild, moderate, and severe disability. RESULTS On the group level, VH scores were comparable between MS and control (p = 0.629). In MS, VH scores declined with disease duration (β = -0.009, p = 0.004) and age (β = -0.007, p = 0.001). There was no relation between VH scores and higher age in HCs. In MS patients, VH was related to normalized gray (NGMV, β = 0.001, p = 0.011) and white matter volume (NWMV, β = 0.001, p = 0.003), macular ganglion cell-inner plexiform layer thickness (mGCIPL, β = 0.006, p < 0.001), and peripapillary retinal nerve fiber layer thickness (pRNFL, β = 0.004, p = 0.008). VH was significantly lower in severe compared to mild disability (mean difference -28.86%, p = 0.058). CONCLUSIONS There is a correlation between VH on OCT and disease duration, more severe disability and lower brain volumes in MS. Biologically, these relationships suggest accelerated glymphatic clearance with disease-related atrophy.
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Affiliation(s)
- Sezgi Kaçar
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (D.C.); (B.M.J.U.); (A.P.); (E.M.M.S.)
- Dutch Expertise Center for Neuro-Ophthalmology, VU University Medical Center, 1081 HV Amsterdam, The Netherlands
| | - Danko Coric
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (D.C.); (B.M.J.U.); (A.P.); (E.M.M.S.)
- Dutch Expertise Center for Neuro-Ophthalmology, VU University Medical Center, 1081 HV Amsterdam, The Netherlands
| | - Giovanni Ometto
- Department of Optometry and Visual Sciences, City, University of London, London WC1E 7HU, UK; (G.O.); (G.M.); (D.P.C.)
| | - Giovanni Montesano
- Department of Optometry and Visual Sciences, City, University of London, London WC1E 7HU, UK; (G.O.); (G.M.); (D.P.C.)
| | - Alastair K. Denniston
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham B15 2TT, UK;
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham B15 2TT, UK
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London EC1V 9LF, UK;
| | - Pearse A. Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital and UCL Institute of Ophthalmology, London EC1V 9LF, UK;
| | - Bernard M. J. Uitdehaag
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (D.C.); (B.M.J.U.); (A.P.); (E.M.M.S.)
| | - David P. Crabb
- Department of Optometry and Visual Sciences, City, University of London, London WC1E 7HU, UK; (G.O.); (G.M.); (D.P.C.)
| | - Menno M. Schoonheim
- Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Center, 1081 HV Amsterdam, The Netherlands;
| | - Axel Petzold
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (D.C.); (B.M.J.U.); (A.P.); (E.M.M.S.)
- Dutch Expertise Center for Neuro-Ophthalmology, VU University Medical Center, 1081 HV Amsterdam, The Netherlands
- Department of Neurology and Ophthalmology, Moorfields Eye Hospital, City Road, London EC1V 9LF, UK
- The National Hospital for Neurology and Neurosurgery, University College London, London WC1E 7HU, UK
| | - Eva M. M. Strijbis
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, VU University Medical Center, Amsterdam UMC Location VUmc, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands; (D.C.); (B.M.J.U.); (A.P.); (E.M.M.S.)
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Tan TF, Thirunavukarasu AJ, Campbell JP, Keane PA, Pasquale LR, Abramoff MD, Kalpathy-Cramer J, Lum F, Kim JE, Baxter SL, Ting DSW. Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges. Ophthalmol Sci 2023; 3:100394. [PMID: 37885755 PMCID: PMC10598525 DOI: 10.1016/j.xops.2023.100394] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/07/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023]
Abstract
The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: "large language models," "generative artificial intelligence," "ophthalmology," "ChatGPT," and "eye," based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders' perspectives-including patients, physicians, and policymakers-the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Ting Fang Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Arun James Thirunavukarasu
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Corpus Christi College, University of Cambridge, Cambridge, United Kingdom
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Pearse A. Keane
- Moorfields Eye Hospital, University of College London, London, United Kingdom
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Michael D. Abramoff
- American Medical Association's Digital Medicine Payment Advisory Group (DMPAG) Artificial Intelligence Workgroup, American Medical Association, Chicago, Illinois
- Department of Ophthalmology, University of Iowa, Iowa City, Iowa
- Digital Diagnostics, Inc, Coralville, Iowa
| | | | - Flora Lum
- American Academy of Ophthalmology, San Francisco, California
| | - Judy E. Kim
- Department of Ophthalmology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Byers Eye Institute, Stanford University, Stanford, California
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Brandao-de-Resende C, Melo M, Lee E, Jindal A, Neo YN, Sanghi P, Freitas JR, Castro PV, Rosa VO, Valentim GF, Higino MLO, Hay GR, Keane PA, Vasconcelos-Santos DV, Day AC. A machine learning system to optimise triage in an adult ophthalmic emergency department: a model development and validation study. EClinicalMedicine 2023; 66:102331. [PMID: 38089860 PMCID: PMC10711497 DOI: 10.1016/j.eclinm.2023.102331] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 10/27/2023] [Accepted: 11/07/2023] [Indexed: 12/31/2023] Open
Abstract
Background A substantial proportion of attendances to ophthalmic emergency departments are for non-urgent presentations. We developed and evaluated a machine learning system (DemDx Ophthalmology Triage System: DOTS) to optimise triage, with the aim of reducing inappropriate emergency attendances and streamlining case referral when necessary. Methods DOTS was built using retrospective tabular data from 11,315 attendances between July 1st, 2021, to June 15th, 2022 at Moorfields Eye Hospital Emergency Department (MEH) in London, UK. Demographic and clinical features were used as inputs and a triage recommendation was given ("see immediately", "see within a week", or "see electively"). DOTS was validated temporally and compared with triage nurses' performance (1269 attendances at MEH) and validated externally (761 attendances at the Federal University of Minas Gerais - UFMG, Brazil). It was also tested for biases and robustness to variations in disease incidences. All attendances from patients aged at least 18 years with at least one confirmed diagnosis were included in the study. Findings For identifying ophthalmic emergency attendances, on temporal validation, DOTS had a sensitivity of 94.5% [95% CI 92.3-96.1] and a specificity of 42.4% [38.8-46.1]. For comparison within the same dataset, triage nurses had a sensitivity of 96.4% [94.5-97.7] and a specificity of 25.1% [22.0-28.5]. On external validation at UFMG, DOTS had a sensitivity of 95.2% [92.5-97.0] and a specificity of 32.2% [27.4-37.0]. In simulated scenarios with varying disease incidences, the sensitivity was ≥92.2% and the specificity was ≥36.8%. No differences in sensitivity were found in subgroups of index of multiple deprivation, but the specificity was higher for Q2 when compared to Q4 (Q4 is less deprived than Q2). Interpretation At MEH, DOTS had similar sensitivity to triage nurses in determining attendance priority; however, with a specificity of 17.3% higher, DOTS resulted in lower rates of patients triaged to be seen immediately at emergency. DOTS showed consistent performance in temporal and external validation, in social-demographic subgroups and was robust to varying relative disease incidences. Further trials are necessary to validate these findings. This system will be prospectively evaluated, considering human-computer interaction, in a clinical trial. Funding The Artificial Intelligence in Health and Care Award (AI_AWARD01671) of the NHS AI Lab under National Institute for Health and Care Research (NIHR) and the Accelerated Access Collaborative (AAC).
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Affiliation(s)
- Camilo Brandao-de-Resende
- Institute of Ophthalmology, University College London (UCL), London, UK
- NIHR Moorfields Clinical Research Facility, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Research Department, DemDX Ltd, London, UK
| | - Mariane Melo
- NIHR Moorfields Clinical Research Facility, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Research Department, DemDX Ltd, London, UK
| | - Elsa Lee
- Institute of Ophthalmology, University College London (UCL), London, UK
- NIHR Moorfields Clinical Research Facility, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Research Department, DemDX Ltd, London, UK
| | - Anish Jindal
- Institute of Ophthalmology, University College London (UCL), London, UK
- Accident and Emergency Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Yan N. Neo
- Accident and Emergency Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Priyanka Sanghi
- Accident and Emergency Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Joao R. Freitas
- Research Department, DemDX Ltd, London, UK
- University of Sao Paulo (USP), Sao Paulo, Brazil
| | - Paulo V.I.P. Castro
- Hospital Sao Geraldo, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Victor O.M. Rosa
- Hospital Sao Geraldo, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | | | - Maria Luisa O. Higino
- Hospital Sao Geraldo, Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil
| | - Gordon R. Hay
- Institute of Ophthalmology, University College London (UCL), London, UK
- Accident and Emergency Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Pearse A. Keane
- Institute of Ophthalmology, University College London (UCL), London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | - Alexander C. Day
- Institute of Ophthalmology, University College London (UCL), London, UK
- Accident and Emergency Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Fu DJ, Lipkova V, Liefers B, Glinton S, Faes L, McKeown A, Scheibler L, Pontikos N, Patel PJ, Zhang G, Keane PA, Balaskas K. Evaluating the Effects of C3 Inhibition on Geographic Atrophy Progression from Deep-Learning OCT Quantification: A Split-Person Study. Ophthalmol Ther 2023; 12:3143-3158. [PMID: 37715860 PMCID: PMC10640460 DOI: 10.1007/s40123-023-00798-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/15/2023] [Indexed: 09/18/2023] Open
Abstract
INTRODUCTION To evaluate the effect pegcetacoplan, a C3 and C3b inhibitor, on the rate of progression of geographic atrophy (GA) as assessed by spectral domain optical coherence tomography (SD-OCT) using a split-person study design and deep-learning quantification. METHODS A post hoc analysis of phase 2 FILLY trial data comparing study (treated monthly, treated every other month and sham-treated) and fellow (untreated) eyes in a split-person study design was performed. This analysis included 288 eyes from 144 patients with bilateral GA from the FILLY phase 2 trial (Clinical Trials identifier: NCT02503332). Only patients with bilateral GA and without evidence of choroidal neovascularisation in either eye were included. Patient study eyes were treated with sham injections or with pegcetacoplan monthly (PM) or every other month (PEOM) for 12 months. SD-OCT scans of study and fellow eyes taken at baseline and 12 months were used for the analysis. The main outcomes were the annual change in the area of retinal pigment epithelial and outer retinal atrophy (RORA), its constituent features (photoreceptor degeneration [PRD], retinal pigment epithelium [RPE] loss, hypertransmission) and intact macula as compared to the untreated fellow eye. RESULTS Annual GA growth was reduced in eyes treated with PM versus untreated fellow eyes for OCT features, including RORA (study eye 0.792 vs. fellow eye 1.13 mm2; P = 0.003), PRD (0.739 vs. 1.23 mm2; P = 0.015), RPE-loss (0.789 vs. 1.17 mm2; P = 0.007) and intact macula (- 0.735 vs. - 1.29 mm2; P = 0.011). Similar (but not statistically significant) trends were observed with the PEOM treatment or when GA was quantified with fundus autofluorescence (FAF). The sham treatment demonstrated no effect. Pearson correlation coefficients showed concordance in the enlargement rate of GA between the study and fellow eyes in the sham (R = 0.64) and PEOM (R = 0.68) groups, but not in the PM group (R = 0.21). CONCLUSIONS Pegcetacoplan-treated eyes demonstrated a reduction in spatial GA progression compared to their untreated counterparts. This effect was more evident on OCT than with FAF. TRIAL REGISTRATION Clinical Trials identifier: NCT02503332.
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Affiliation(s)
- Dun Jack Fu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK.
| | - Veronika Lipkova
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | - Bart Liefers
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sophie Glinton
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | - Livia Faes
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | | | | | - Nikolas Pontikos
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | - Praveen J Patel
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | - Gongyu Zhang
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust-UCL Institute of Ophthalmology, 162 City Rd, London, EC1V 2PD, UK
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Antaki F, Milad D, Chia MA, Giguère CÉ, Touma S, El-Khoury J, Keane PA, Duval R. Capabilities of GPT-4 in ophthalmology: an analysis of model entropy and progress towards human-level medical question answering. Br J Ophthalmol 2023:bjo-2023-324438. [PMID: 37923374 DOI: 10.1136/bjo-2023-324438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 10/01/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND Evidence on the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model (LLM), in the ophthalmology question-answering domain is needed. METHODS We tested GPT-4 on two 260-question multiple choice question sets from the Basic and Clinical Science Course (BCSC) Self-Assessment Program and the OphthoQuestions question banks. We compared the accuracy of GPT-4 models with varying temperatures (creativity setting) and evaluated their responses in a subset of questions. We also compared the best-performing GPT-4 model to GPT-3.5 and to historical human performance. RESULTS GPT-4-0.3 (GPT-4 with a temperature of 0.3) achieved the highest accuracy among GPT-4 models, with 75.8% on the BCSC set and 70.0% on the OphthoQuestions set. The combined accuracy was 72.9%, which represents an 18.3% raw improvement in accuracy compared with GPT-3.5 (p<0.001). Human graders preferred responses from models with a temperature higher than 0 (more creative). Exam section, question difficulty and cognitive level were all predictive of GPT-4-0.3 answer accuracy. GPT-4-0.3's performance was numerically superior to human performance on the BCSC (75.8% vs 73.3%) and OphthoQuestions (70.0% vs 63.0%), but the difference was not statistically significant (p=0.55 and p=0.09). CONCLUSION GPT-4, an LLM trained on non-ophthalmology-specific data, performs significantly better than its predecessor on simulated ophthalmology board-style exams. Remarkably, its performance tended to be superior to historical human performance, but that difference was not statistically significant in our study.
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Affiliation(s)
- Fares Antaki
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, UCL, London, UK
- The CHUM School of Artificial Intelligence in Healthcare, Montreal, Quebec, Canada
- Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada
- Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal (CHUM), Montreal, Quebec, Canada
| | - Daniel Milad
- Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada
- Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal (CHUM), Montreal, Quebec, Canada
- Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, Canada
| | - Mark A Chia
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, UCL, London, UK
| | | | - Samir Touma
- Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada
- Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal (CHUM), Montreal, Quebec, Canada
- Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, Canada
| | - Jonathan El-Khoury
- Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada
- Department of Ophthalmology, Centre Hospitalier de l'Universite de Montreal (CHUM), Montreal, Quebec, Canada
- Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, Canada
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, UCL, London, UK
- NIHR Moorfields Biomedical Research Centre, London, UK
| | - Renaud Duval
- Department of Ophthalmology, University of Montreal, Montreal, Quebec, Canada
- Department of Ophthalmology, Hopital Maisonneuve-Rosemont, Montreal, Quebec, Canada
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Dow ER, Jeong HK, Katz EA, Toth CA, Wang D, Lee T, Kuo D, Allingham MJ, Hadziahmetovic M, Mettu PS, Schuman S, Carin L, Keane PA, Henao R, Lad EM. A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography. JAMA Ophthalmol 2023; 141:1052-1061. [PMID: 37856139 PMCID: PMC10587827 DOI: 10.1001/jamaophthalmol.2023.4659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 08/27/2023] [Indexed: 10/20/2023]
Abstract
Importance The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans. Objective To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan. Design, Setting, and Participants This retrospective cohort study included participants with iAMD at baseline and who either progressed or did not progress to GA within the subsequent 13 months. Participants were included from centers in 4 US states. Data set 1 included patients from the Age-Related Eye Disease Study 2 AREDS2 (Ancillary Spectral-Domain Optical Coherence Tomography) A2A study (July 2008 to August 2015). Data sets 2 and 3 included patients with imaging taken in routine clinical care at a tertiary referral center and associated satellites between January 2013 and January 2023. The stored imaging data were retrieved for the purpose of this study from July 1, 2022, to February 1, 2023. Data were analyzed from May 2021 to July 2023. Exposure A position-aware convolutional neural network with proactive pseudointervention was trained and cross-validated on Bioptigen SD-OCT volumes (data set 1) and validated on 2 external data sets comprising Heidelberg Spectralis SD-OCT scans (data sets 2 and 3). Main Outcomes and Measures Prediction of progression to GA within 13 months was evaluated with area under the receiver-operator characteristic curves (AUROC) as well as area under the precision-recall curve (AUPRC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results The study included a total of 417 patients: 316 in data set 1 (mean [SD] age, 74 [8]; 185 [59%] female), 53 in data set 2, (mean [SD] age, 83 [8]; 32 [60%] female), and 48 in data set 3 (mean [SD] age, 81 [8]; 32 [67%] female). The AUROC for prediction of progression from iAMD to GA within 1 year was 0.94 (95% CI, 0.92-0.95; AUPRC, 0.90 [95% CI, 0.85-0.95]; sensitivity, 0.88 [95% CI, 0.84-0.92]; specificity, 0.90 [95% CI, 0.87-0.92]) for data set 1. The addition of expert-annotated SD-OCT features to the model resulted in no improvement compared to the fully autonomous model (AUROC, 0.95; 95% CI, 0.92-0.95; P = .19). On an independent validation data set (data set 2), the model predicted progression to GA with an AUROC of 0.94 (95% CI, 0.91-0.96; AUPRC, 0.92 [0.89-0.94]; sensitivity, 0.91 [95% CI, 0.74-0.98]; specificity, 0.80 [95% CI, 0.63-0.91]). At a high-specificity operating point, simulated clinical trial recruitment was enriched for patients progressing to GA within 1 year by 8.3- to 20.7-fold (data sets 2 and 3). Conclusions and Relevance The fully automated, position-aware deep-learning algorithm assessed in this study successfully predicted progression from iAMD to GA over a clinically meaningful time frame. The ability to predict imminent GA progression could facilitate clinical trials aimed at preventing the condition and could guide clinical decision-making regarding screening frequency or treatment initiation.
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Affiliation(s)
- Eliot R. Dow
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Hyeon Ki Jeong
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
| | - Ella Arnon Katz
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Cynthia A. Toth
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Dong Wang
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Terry Lee
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - David Kuo
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Michael J. Allingham
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Majda Hadziahmetovic
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Priyatham S. Mettu
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Stefanie Schuman
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Lawrence Carin
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Pearse A. Keane
- University College London Institute of Ophthalmology, National Institute for Health and Care Research, Biomedical Research Centre, Moorfields Eye Hospital National Health Services Foundation Trust, London, United Kingdom
| | - Ricardo Henao
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Eleonora M. Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
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21
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Korot E, Gonçalves MB, Huemer J, Beqiri S, Khalid H, Kelly M, Chia M, Mathijs E, Struyven R, Moussa M, Keane PA. Clinician-Driven AI: Code-Free Self-Training on Public Data for Diabetic Retinopathy Referral. JAMA Ophthalmol 2023; 141:1029-1036. [PMID: 37856110 PMCID: PMC10587830 DOI: 10.1001/jamaophthalmol.2023.4508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 08/23/2023] [Indexed: 10/20/2023]
Abstract
Importance Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. Objective To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. Design, Setting, and Participants This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. Exposures Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. Main Outcomes and Measures The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. Results For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. Conclusions and Relevance These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.
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Affiliation(s)
- Edward Korot
- Retina Specialists of Michigan, Grand Rapids
- Moorfields Eye Hospital, London, United Kingdom
- Stanford University Byers Eye Institute, Palo Alto, California
| | - Mariana Batista Gonçalves
- Moorfields Eye Hospital, London, United Kingdom
- Federal University of Sao Paulo, Sao Paulo, Brazil
- Instituto da Visão, Sao Paulo, Brazil
| | | | - Sara Beqiri
- Moorfields Eye Hospital, London, United Kingdom
- University College London Medical School, London, United Kingdom
| | - Hagar Khalid
- Moorfields Eye Hospital, London, United Kingdom
- Ophthalmology Department, Faculty of Medicine, Tanta University Hospital, Tanta, Gharbia, Egypt
| | - Madeline Kelly
- Moorfields Eye Hospital, London, United Kingdom
- University College London Medical School, London, United Kingdom
- UCL Centre for Medical Image Computing, London, United Kingdom
| | - Mark Chia
- Moorfields Eye Hospital, London, United Kingdom
| | - Emily Mathijs
- Michigan State University College of Osteopathic Medicine, East Lansing
| | | | - Magdy Moussa
- Ophthalmology Department, Faculty of Medicine, Tanta University Hospital, Tanta, Gharbia, Egypt
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22
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Thirunavukarasu AJ, Elangovan K, Gutierrez L, Li Y, Tan I, Keane PA, Korot E, Ting DSW. Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial. J Med Internet Res 2023; 25:e49949. [PMID: 37824185 PMCID: PMC10603560 DOI: 10.2196/49949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/21/2023] [Accepted: 09/13/2023] [Indexed: 10/13/2023] Open
Abstract
Deep learning-based clinical imaging analysis underlies diagnostic artificial intelligence (AI) models, which can match or even exceed the performance of clinical experts, having the potential to revolutionize clinical practice. A wide variety of automated machine learning (autoML) platforms lower the technical barrier to entry to deep learning, extending AI capabilities to clinicians with limited technical expertise, and even autonomous foundation models such as multimodal large language models. Here, we provide a technical overview of autoML with descriptions of how autoML may be applied in education, research, and clinical practice. Each stage of the process of conducting an autoML project is outlined, with an emphasis on ethical and technical best practices. Specifically, data acquisition, data partitioning, model training, model validation, analysis, and model deployment are considered. The strengths and limitations of available code-free, code-minimal, and code-intensive autoML platforms are considered. AutoML has great potential to democratize AI in medicine, improving AI literacy by enabling "hands-on" education. AutoML may serve as a useful adjunct in research by facilitating rapid testing and benchmarking before significant computational resources are committed. AutoML may also be applied in clinical contexts, provided regulatory requirements are met. The abstraction by autoML of arduous aspects of AI engineering promotes prioritization of data set curation, supporting the transition from conventional model-driven approaches to data-centric development. To fulfill its potential, clinicians must be educated on how to apply these technologies ethically, rigorously, and effectively; this tutorial represents a comprehensive summary of relevant considerations.
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Affiliation(s)
- Arun James Thirunavukarasu
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Kabilan Elangovan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Laura Gutierrez
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Yong Li
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Iris Tan
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Edward Korot
- Byers Eye Institute, Stanford University, Palo Alto, CA, United States
- Retina Specialists of Michigan, Grand Rapids, MI, United States
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation Research Group, Singapore Eye Research Institute, Singapore, Singapore
- Byers Eye Institute, Stanford University, Palo Alto, CA, United States
- Singapore National Eye Centre, Singapore, Singapore
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23
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Zhou Y, Chia MA, Wagner SK, Ayhan MS, Williamson DJ, Struyven RR, Liu T, Xu M, Lozano MG, Woodward-Court P, Kihara Y, Altmann A, Lee AY, Topol EJ, Denniston AK, Alexander DC, Keane PA. A foundation model for generalizable disease detection from retinal images. Nature 2023; 622:156-163. [PMID: 37704728 PMCID: PMC10550819 DOI: 10.1038/s41586-023-06555-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 08/18/2023] [Indexed: 09/15/2023]
Abstract
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.
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Affiliation(s)
- Yukun Zhou
- Centre for Medical Image Computing, University College London, London, UK.
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK.
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
| | - Mark A Chia
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Siegfried K Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Murat S Ayhan
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Dominic J Williamson
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Robbert R Struyven
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Timing Liu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Moucheng Xu
- Centre for Medical Image Computing, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Mateo G Lozano
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Computer Science, University of Coruña, A Coruña, Spain
| | - Peter Woodward-Court
- Centre for Medical Image Computing, University College London, London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Yuka Kihara
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA, USA
| | - Andre Altmann
- Centre for Medical Image Computing, University College London, London, UK
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, University of Washington, Seattle, WA, USA
| | - Eric J Topol
- Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
| | - Alastair K Denniston
- Academic Unit of Ophthalmology, University of Birmingham, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK.
- Institute of Ophthalmology, University College London, London, UK.
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24
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Hannaway N, Zarkali A, Leyland LA, Bremner F, Nicholas JM, Wagner SK, Roig M, Keane PA, Toosy A, Chataway J, Weil RS. Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease. J Neurol Neurosurg Psychiatry 2023; 94:742-750. [PMID: 37080759 PMCID: PMC10447370 DOI: 10.1136/jnnp-2023-331083] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/30/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence. METHODS We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods. RESULTS Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12). CONCLUSIONS In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials.
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Affiliation(s)
- Naomi Hannaway
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Fion Bremner
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Matthew Roig
- UCL Queen Square Institute of Neurology, London, UK
| | - Pearse A Keane
- UCL Queen Square Institute of Neurology, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Ahmed Toosy
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
- MRC CTU at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
- Movement Disorders Centre, University College London, London, UK
| | - Rimona Sharon Weil
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
- Movement Disorders Centre, University College London, London, UK
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
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25
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Merle DA, Sen M, Armento A, Stanton CM, Thee EF, Meester-Smoor MA, Kaiser M, Clark SJ, Klaver CCW, Keane PA, Wright AF, Ehrmann M, Ueffing M. 10q26 - The enigma in age-related macular degeneration. Prog Retin Eye Res 2023; 96:101154. [PMID: 36513584 DOI: 10.1016/j.preteyeres.2022.101154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 11/21/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Despite comprehensive research efforts over the last decades, the pathomechanisms of age-related macular degeneration (AMD) remain far from being understood. Large-scale genome wide association studies (GWAS) were able to provide a defined set of genetic aberrations which contribute to disease risk, with the strongest contributors mapping to distinct regions on chromosome 1 and 10. While the chromosome 1 locus comprises factors of the complement system with well-known functions, the role of the 10q26-locus in AMD-pathophysiology remains enigmatic. 10q26 harbors a cluster of three functional genes, namely PLEKHA1, ARMS2 and HTRA1, with most of the AMD-associated genetic variants mapping to the latter two genes. High linkage disequilibrium between ARMS2 and HTRA1 has kept association studies from reliably defining the risk-causing gene for long and only very recently the genetic risk region has been narrowed to ARMS2, suggesting that this is the true AMD gene at this locus. However, genetic associations alone do not suffice to prove causality and one or more of the 14 SNPs on this haplotype may be involved in long-range control of gene expression, leaving HTRA1 and PLEKHA1 still suspects in the pathogenic pathway. Both, ARMS2 and HTRA1 have been linked to extracellular matrix homeostasis, yet their exact molecular function as well as their role in AMD pathogenesis remains to be uncovered. The transcriptional regulation of the 10q26 locus adds an additional level of complexity, given, that gene-regulatory as well as epigenetic alterations may influence expression levels from 10q26 in diseased individuals. Here, we provide a comprehensive overview on the 10q26 locus and its three gene products on various levels of biological complexity and discuss current and future research strategies to shed light on one of the remaining enigmatic spots in the AMD landscape.
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Affiliation(s)
- David A Merle
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department of Ophthalmology, Medical University of Graz, 8036, Graz, Austria.
| | - Merve Sen
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany
| | - Angela Armento
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany
| | - Chloe M Stanton
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Eric F Thee
- Department of Ophthalmology, Erasmus University Medical Center, 3015GD, Rotterdam, Netherlands; Department of Epidemiology, Erasmus University Medical Center, 3015CE, Rotterdam, Netherlands
| | - Magda A Meester-Smoor
- Department of Ophthalmology, Erasmus University Medical Center, 3015GD, Rotterdam, Netherlands; Department of Epidemiology, Erasmus University Medical Center, 3015CE, Rotterdam, Netherlands
| | - Markus Kaiser
- Center of Medical Biotechnology, Faculty of Biology, University Duisburg-Essen, 45117, Essen, Germany
| | - Simon J Clark
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Lydia Becker Institute of Immunology and Inflammation, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK
| | - Caroline C W Klaver
- Department of Ophthalmology, Erasmus University Medical Center, 3015GD, Rotterdam, Netherlands; Department of Epidemiology, Erasmus University Medical Center, 3015CE, Rotterdam, Netherlands; Department of Ophthalmology, Radboudumc, 6525EX, Nijmegen, Netherlands; Institute of Molecular and Clinical Ophthalmology Basel, CH-4031, Basel, Switzerland
| | - Pearse A Keane
- Institute for Health Research, Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, EC1V 2PD, UK
| | - Alan F Wright
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Michael Ehrmann
- Center of Medical Biotechnology, Faculty of Biology, University Duisburg-Essen, 45117, Essen, Germany
| | - Marius Ueffing
- Institute for Ophthalmic Research, Department for Ophthalmology, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany; Department for Ophthalmology, University Eye Clinic, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany.
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26
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Affiliation(s)
- Mark A Chia
- University College London, London, England
- Moorfields Eye Hospital, London, England
| | - Pearse A Keane
- University College London, London, England
- Moorfields Eye Hospital, London, England
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27
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Wagner SK, Zhou Y, O'byrne C, Khawaja AP, Petzold A, Keane PA. Comment on "Race distribution in non-arteritic anterior ischemic optic neuropathy". Am J Ophthalmol 2023; 252:326-327. [PMID: 37149243 DOI: 10.1016/j.ajo.2023.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 04/07/2023] [Indexed: 05/08/2023]
Affiliation(s)
- Siegfried K Wagner
- Institute of Ophthalmology, University College London, NIHR Moorfields Biomedical Research Centre, London, United Kingdom
| | - Yukun Zhou
- Institute of Ophthalmology, University College London, NIHR Moorfields Biomedical Research Centre, Centre for Medical Image Computing, Department of Computer Science, London, United Kingdom
| | - Ciara O'byrne
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
| | - Anthony P Khawaja
- Institute of Ophthalmology, University College London, NIHR Moorfields Biomedical Research Centre, London, United Kingdom
| | - Axel Petzold
- Institute of Ophthalmology, University College London, NIHR Moorfields Biomedical Research Centre, Queen Square Institute of Neurology, London, United Kingdom
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, NIHR Moorfields Biomedical Research Centre, London, United Kingdom
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28
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Kelly B, Martinez M, Do H, Hayden J, Huang Y, Yedavalli V, Ho C, Keane PA, Killeen R, Lawlor A, Moseley ME, Yeom KW, Lee EH. DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy. Eur Radiol 2023; 33:5728-5739. [PMID: 36847835 PMCID: PMC10326097 DOI: 10.1007/s00330-023-09478-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 11/23/2022] [Accepted: 01/19/2023] [Indexed: 03/01/2023]
Abstract
OBJECTIVES Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion. METHODS All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy. RESULTS In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71. CONCLUSIONS Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention). KEY POINTS • DEEP MOVEMENT represents a novel application of a model applied to acute stroke imaging to handle two types of temporal complexity, dynamic video and pre and post intervention. • The model takes as an input digital subtraction angiograms of the anterior cerebral circulation and classifies according to (1) the presence or absence of large vessel occlusion, (2) the location of the occlusion, and (3) the efficacy of thrombectomy. • Potential clinical utility lies in providing decision support via rapid interpretation (pre thrombectomy) and automated objective gradation of thrombectomy outcomes (post thrombectomy).
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Affiliation(s)
- Brendan Kelly
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Radiology, St Vincent's University Hospital, Elm Park, Dublin 4, Ireland.
- Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland.
| | - Mesha Martinez
- Department of Clinical Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Huy Do
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Yuhao Huang
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Vivek Yedavalli
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chang Ho
- Department of Clinical Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pearse A Keane
- Moorfields Eye Hospital, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Ronan Killeen
- Department of Radiology, St Vincent's University Hospital, Elm Park, Dublin 4, Ireland
| | - Aonghus Lawlor
- Insight Centre for Data Analytics, University College Dublin, Belfield, Dublin 4, Ireland
| | - Michael E Moseley
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kristen W Yeom
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Edward H Lee
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
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29
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Sevgi M, Keane PA. Ophthalmology's new horizon: Moving from reactive care to proactive artificial intelligence solutions. Saudi J Ophthalmol 2023; 37:171-172. [PMID: 38074309 PMCID: PMC10701142 DOI: 10.4103/sjopt.sjopt_245_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 03/14/2024] Open
Affiliation(s)
- Mertcan Sevgi
- Institute of Ophthalmology, University College London, UK
| | - Pearse A. Keane
- Institute of Ophthalmology, University College London, UK
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Ferraz DA, Guan Z, Costa EA, Martins E, Keane PA, Ting DSW, Belfort R, Scherer R, Koh V, Muccioli C. Proposal of a new slit-lamp shield for ophthalmic examination and assessment of its effectiveness using computational simulations. Arq Bras Oftalmol 2023; 86:322-329. [PMID: 35544928 DOI: 10.5935/0004-2749.20230058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 10/20/2021] [Indexed: 07/20/2023] Open
Abstract
PURPOSE This study aimed to use computational models for simulating the movement of respiratory droplets when assessing the efficacy of standard slit-lamp shield versus a new shield designed for increased clinician comfort as well as adequate protection. METHODS Simulations were performed using the commercial software Star-CCM+. Respiratory droplets were assumed to be 100% water in volume fraction with particle diameter distribution represented by a geometric mean of 74.4 (±1.5 standard deviation) μm over a 4-min duration. The total mass of respiratory droplets expelled from patients' mouths and droplet accumulation on the manikin were measured under the following three conditions: with no slit-lamp shield, using the standard slit-lamp shield, and using our new proposed shield. RESULTS The total accumulated water droplet mass (kilogram) and percentage of expelled mass accumulated on the shield under the three aforementioned conditions were as follows: 5.84e-10 kg (28% of the total weight of particle emitted that settled on the manikin), 9.14e-13 kg (0.045%), and 3.19e-13 (0.015%), respectively. The standard shield could shield off 99.83% of the particles that would otherwise be deposited on the manikin, which is comparable to 99.95% for the proposed design. Conclusion: Slit-lamp shields are effective infection control tools against respiratory droplets. The proposed shield showed comparable effectiveness compared with conventional slit-lamp shields, but with potentially enhanced ergonomics for ophthalmologists during slit-lamp examinations.
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Affiliation(s)
- Daniel Araújo Ferraz
- Department of Ophthalmology, Universidade Federal de São Paulo, São Paulo, SP, Brazil
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Zeyu Guan
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Edinilson A Costa
- Department of Mechanical Engineering, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Eduardo Martins
- Department of Biomedical Engineering Yonsei University, South Korea
| | - Pearse A Keane
- NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Rubens Belfort
- Department of Ophthalmology, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Rafael Scherer
- Department of Ophthalmology, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, Singapore
| | - Cristina Muccioli
- Department of Ophthalmology, Universidade Federal de São Paulo, São Paulo, SP, Brazil
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Gokhale KM, Adderley NJ, Subramanian A, Lee WH, Han D, Coker J, Braithwaite T, Denniston AK, Keane PA, Nirantharakumar K. Metformin and risk of age-related macular degeneration in individuals with type 2 diabetes: a retrospective cohort study. Br J Ophthalmol 2023; 107:980-986. [PMID: 35115301 DOI: 10.1136/bjophthalmol-2021-319641] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 01/20/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Age-related macular degeneration (AMD) in its late stages is a leading cause of sight loss in developed countries. Some previous studies have suggested that metformin may be associated with a reduced risk of developing AMD, but the evidence is inconclusive. AIMS To explore the relationship between metformin use and development of AMD among patients with type 2 diabetes in the UK. METHODS A large, population-based retrospective open cohort study with a time-dependent exposure design was carried out using IQVIA Medical Research Data, 1995-2019. Patients aged ≥40 with diagnosed type 2 diabetes were included.The exposed group was those prescribed metformin (with or without any other antidiabetic medications); the comparator (unexposed) group was those prescribed other antidiabetic medications only. The exposure status was treated as time varying, collected at 3-monthly time intervals.Extended Cox proportional hazards regression was used to calculate the adjusted HRs for development of the outcome, newly diagnosed AMD. RESULTS A total of 173 689 patients, 57% men, mean (SD) age 62.8 (11.6) years, with incident type 2 diabetes and a record of one or more antidiabetic medications were included in the study. Median follow-up was 4.8 (IQR 2.3-8.3, range 0.5-23.8) years. 3111 (1.8%) patients developed AMD. The adjusted HR for diagnosis of AMD was 1.02 (95% CI 0.92 to 1.12) in patients prescribed metformin (with or without other antidiabetic medications) compared with those prescribed any other antidiabetic medication only. CONCLUSION We found no evidence that metformin was associated with risk of AMD in primary care patients requiring treatment for type 2 diabetes.
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Affiliation(s)
- Krishna M Gokhale
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Nicola J Adderley
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | | | - Wen Hwa Lee
- Action Against Age-Related Macular Degeneration, London, UK
| | - Diana Han
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jesse Coker
- Action Against Age-Related Macular Degeneration, London, UK
| | - Tasanee Braithwaite
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- The School of Immunology and Microbial Sciences and The School of Life Course Sciences, King's College London, London, UK
- The Medical Eye Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Alastair K Denniston
- Department of Ophthalmology, University Hospitals Birmingham NHSFT, Birmingham, UK
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
- Health Data Research UK, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK
| | - Krishnarajah Nirantharakumar
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- Health Data Research UK, London, UK
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Sharma A, Holz FG, Regillo CD, Freund KB, Sarraf D, Khanani AM, Baumal C, Holekamp N, Tadayoni R, Kumar N, Parachuri N, Kuppermann BD, Bandello F, Querques G, Loewenstein A, Özdek Ş, Rezai K, Laurent K, Bilgic A, Lanzetta P, Zur D, Yannuzzi N, Corradetti G, Kaiser P, Hilely A, Boyer D, Rachitskaya A, Chakravarthy U, Wintergerst M, Sarao V, Parolini B, Mruthyunjaya P, Nguyen QD, Do D, Keane PA, Hassan T, Sridhar J, Eichenbaum D, Grewal D, Splitzer M. Biosimilars for retinal diseases: United States-Europe awareness survey (Bio-USER - survey). Expert Opin Biol Ther 2023; 23:851-859. [PMID: 36726203 DOI: 10.1080/14712598.2023.2176218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 01/31/2023] [Indexed: 02/03/2023]
Abstract
PURPOSE To assess the awareness of biosimilar intravitreal anti-VEGF agents among retina specialists practicing in the United States (US) and Europe. METHODS A 16-question online survey was created in English and distributed between Dec 01, 2021 and Jan 31, 2022. A total of 112 respondents (retinal physicians) from the US and Europe participated. RESULTS The majority of the physicians (56.3%) were familiar with anti-VEGF biosimilars. A significant number of physicians needed more information (18.75%) and real world data (25%) before switching to a biosimilar. About one half of the physicians were concerned about biosimilar safety (50%), efficacy (58.9 %), immunogenicity (50%), and their efficacy with extrapolated indications (67.8 %). Retinal physicians from the US were less inclined to shift from off-label bevacizumab to biosimilar ranibizumab or on-label bevacizumab (if approved) compared to physicians from Europe (p=0.0001). Furthermore, physicians from the US were more concerned about biosimilar safety (p=0.0371) and efficacy compared to Europe (p= 0.0078). CONCLUSIONS The Bio-USER survey revealed that while the majority of retinal physicians need additional information regarding the safety, efficacy and immunogenicity when making clinical decisions regarding their use. Retinal physicians from US are more comfortable in continuing to use off-label bevacizumab compared to physicians from Europe.
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Affiliation(s)
| | - Frank G Holz
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Carl D Regillo
- The Retina Service of Wills Eye Hospital, Philadelphia, Pennsylvania, USA
| | - K Bailey Freund
- Vitreous Retina Macula Consultants of New York, New York, USA
- Department of Ophthalmology, NYU Grossman School of Medicine, New York, NY, USA
| | - David Sarraf
- Stein Eye Institute, UCLA, Los Angeles, CA, USA
- Department of Ophthalmology, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA
| | - Arshad M Khanani
- Sierra Eye Associates, Reno, NV, USA and The University of Nevada, Reno School of Medicine, Reno, NV, USA
| | - Caroline Baumal
- Tufts University School of Medicine, New England Eye Center, Boston, Massachusetts
| | | | - Ramin Tadayoni
- Université Paris Cité, AP-HP, Lariboisière, St Louis and Fondation Adolphe de Rothschild hospitals, Paris, France
| | | | | | - Baruch D Kuppermann
- Gavin Herbert Eye Institute, University of California, Irvine, Irvine, CA, USA
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Kleerekooper I, Wagner SK, Trip SA, Plant GT, Petzold A, Keane PA, Khawaja AP. Differentiating glaucoma from chiasmal compression using optical coherence tomography: the macular naso-temporal ratio. Br J Ophthalmol 2023:bjo-2023-323529. [PMID: 37385651 DOI: 10.1136/bjo-2023-323529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/06/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND/AIMS The analysis of visual field loss patterns is clinically useful to guide differential diagnosis of visual pathway pathology. This study investigates whether a novel index of macular atrophy patterns can discriminate between chiasmal compression and glaucoma. METHODS A retrospective series of patients with preoperative chiasmal compression, primary open-angle glaucoma (POAG) and healthy controls. Macular optical coherence tomography (OCT) images were analysed for the macular ganglion cell and inner plexiform layer (mGCIPL) thickness. The nasal hemi-macula was compared with the temporal hemi-macula to derive the macular naso-temporal ratio (mNTR). Differences between groups and diagnostic accuracy were explored with multivariable linear regression and the area under the receiver operating characteristic curve (AUC). RESULTS We included 111 individuals (31 with chiasmal compression, 30 with POAG and 50 healthy controls). Compared with healthy controls, the mNTR was significantly greater in POAG cases (β=0.07, 95% CI 0.03 to 0.11, p=0.001) and lower in chiasmal compression cases (β=-0.12, 95% CI -0.16 to -0.09, p<0.001), even though overall mGCIPL thickness did not discriminate between these pathologies (p=0.36). The mNTR distinguished POAG from chiasmal compression with an AUC of 95.3% (95% CI 90% to 100%). The AUCs when comparing healthy controls to POAG and chiasmal compression were 79.0% (95% CI 68% to 90%) and 89.0% (95% CI 80% to 98%), respectively. CONCLUSIONS The mNTR can distinguish between chiasmal compression and POAG with high discrimination. This ratio may provide utility over-and-above previously reported sectoral thinning metrics. Incorporation of mNTR into the output of OCT instruments may aid earlier diagnosis of chiasmal compression.
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Affiliation(s)
- Iris Kleerekooper
- Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Dutch Expertise Centre for Neuro-ophthalmology & MS Centre, Departments of Neurology and Ophthalmology, Amsterdam UMC, Amsterdam, Netherlands
| | - Siegfried K Wagner
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - S Anand Trip
- Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- University College London Hospitals (UCLH) NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London, UK
| | - Gordon T Plant
- Department of Brain Repair and Rehabilitation, Institute of Neurology, University College London, London, UK
| | - Axel Petzold
- Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurophthalmology, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Dutch Expertise Centre for Neuro-ophthalmology & MS Centre, Departments of Neurology and Ophthalmology, Amsterdam UMC, Amsterdam, Netherlands
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Anthony P Khawaja
- Institute of Ophthalmology, University College London, London, UK
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
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Heindl LM, Li S, Ting DSW, Keane PA. Artificial intelligence in ophthalmological practice: when ideal meets reality. BMJ Open Ophthalmol 2023; 8:e001129. [PMID: 37493688 PMCID: PMC10255244 DOI: 10.1136/bmjophth-2022-001129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023] Open
Affiliation(s)
- Ludwig M Heindl
- Department of Ophthalmology, University of Cologne, Koln, Germany
| | - Senmao Li
- Department of Ophthalmology, University of Cologne, Koln, Germany
- Department of Ophthalmology, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China
| | - Daniel S W Ting
- Singapore National Eye Center, Duke-NUS Medical School, Singapore
- Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore
| | - Pearse A Keane
- Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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35
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Lemaître S, Keane PA. Bilateral diffuse bear tracks. J Fr Ophtalmol 2023; 46:669-670. [PMID: 37080798 DOI: 10.1016/j.jfo.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/06/2022] [Accepted: 10/10/2022] [Indexed: 04/22/2023]
Affiliation(s)
- S Lemaître
- Ocular Oncology Service, Moorfields Eye Hospital NHS Foundation Trust, 162, City road, EC1V 2PD London, United Kingdom.
| | - P A Keane
- NIHR Biomedical Research Center at Moorfields Eye Hospital, NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
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36
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Veturi YA, Woof W, Lazebnik T, Moghul I, Woodward-Court P, Wagner SK, Cabral de Guimarães TA, Daich Varela M, Liefers B, Patel PJ, Beck S, Webster AR, Mahroo O, Keane PA, Michaelides M, Balaskas K, Pontikos N. SynthEye: Investigating the Impact of Synthetic Data on Artificial Intelligence-assisted Gene Diagnosis of Inherited Retinal Disease. Ophthalmol Sci 2023; 3:100258. [PMID: 36685715 PMCID: PMC9852957 DOI: 10.1016/j.xops.2022.100258] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 11/08/2022] [Accepted: 11/09/2022] [Indexed: 11/23/2022]
Abstract
Purpose Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving artificial intelligence-enabled diagnosis of IRDs using generative adversarial networks (GANs). Design Diagnostic study of gene-labeled fundus autofluorescence (FAF) IRD images using deep learning. Participants Moorfields Eye Hospital (MEH) dataset of 15 692 FAF images obtained from 1800 patients with confirmed genetic diagnosis of 1 of 36 IRD genes. Methods A StyleGAN2 model is trained on the IRD dataset to generate 512 × 512 resolution images. Convolutional neural networks are trained for classification using different synthetically augmented datasets, including real IRD images plus 1800 and 3600 synthetic images, and a fully rebalanced dataset. We also perform an experiment with only synthetic data. All models are compared against a baseline convolutional neural network trained only on real data. Main Outcome Measures We evaluated synthetic data quality using a Visual Turing Test conducted with 4 ophthalmologists from MEH. Synthetic and real images were compared using feature space visualization, similarity analysis to detect memorized images, and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) score for no-reference-based quality evaluation. Convolutional neural network diagnostic performance was determined on a held-out test set using the area under the receiver operating characteristic curve (AUROC) and Cohen's Kappa (κ). Results An average true recognition rate of 63% and fake recognition rate of 47% was obtained from the Visual Turing Test. Thus, a considerable proportion of the synthetic images were classified as real by clinical experts. Similarity analysis showed that the synthetic images were not copies of the real images, indicating that copied real images, meaning the GAN was able to generalize. However, BRISQUE score analysis indicated that synthetic images were of significantly lower quality overall than real images (P < 0.05). Comparing the rebalanced model (RB) with the baseline (R), no significant change in the average AUROC and κ was found (R-AUROC = 0.86[0.85-88], RB-AUROC = 0.88[0.86-0.89], R-k = 0.51[0.49-0.53], and RB-k = 0.52[0.50-0.54]). The synthetic data trained model (S) achieved similar performance as the baseline (S-AUROC = 0.86[0.85-87], S-k = 0.48[0.46-0.50]). Conclusions Synthetic generation of realistic IRD FAF images is feasible. Synthetic data augmentation does not deliver improvements in classification performance. However, synthetic data alone deliver a similar performance as real data, and hence may be useful as a proxy to real data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.
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Key Words
- AUROC, area under the receiver operating characteristic curve
- BRISQUE, Blind/Referenceless Image Spatial Quality Evaluator
- Class imbalance
- Clinical Decision-Support Model
- DL, deep learning
- Deep Learning
- FAF, fundas autofluorescence
- FRR, Fake Recognition Rate
- GAN, generative adversarial network
- Generative Adversarial Networks
- IRD, inherited retinal disease
- Inherited Retinal Diseases
- MEH, Moorfields Eye Hospital
- R, baseline model
- RB, rebalanced model
- S, synthetic data trained model
- Synthetic data
- TRR, True Recognition Rate
- UMAP, Universal Manifold Approximation and Projection
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Affiliation(s)
- Yoga Advaith Veturi
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - William Woof
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Teddy Lazebnik
- University College London Cancer Institute, University College London, London, UK
| | | | - Peter Woodward-Court
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Siegfried K. Wagner
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Malena Daich Varela
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | - Stephan Beck
- University College London Cancer Institute, University College London, London, UK
| | - Andrew R. Webster
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Omar Mahroo
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Pearse A. Keane
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Michel Michaelides
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Konstantinos Balaskas
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Nikolas Pontikos
- University College London Institute of Ophthalmology, University College London, London, UK
- Moorfields Eye Hospital, London, UK
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Wagner SK, Liefers B, Radia M, Zhang G, Struyven R, Faes L, Than J, Balal S, Hennings C, Kilduff C, Pooprasert P, Glinton S, Arunakirinathan M, Giannakis P, Braimah IZ, Ahmed ISH, Al-Feky M, Khalid H, Ferraz D, Vieira J, Jorge R, Husain S, Ravelo J, Hinds AM, Henderson R, Patel HI, Ostmo S, Campbell JP, Pontikos N, Patel PJ, Keane PA, Adams G, Balaskas K. Development and international validation of custom-engineered and code-free deep-learning models for detection of plus disease in retinopathy of prematurity: a retrospective study. Lancet Digit Health 2023; 5:e340-e349. [PMID: 37088692 PMCID: PMC10279502 DOI: 10.1016/s2589-7500(23)00050-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 01/08/2023] [Accepted: 02/14/2023] [Indexed: 04/25/2023]
Abstract
BACKGROUND Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed through interval screening by paediatric ophthalmologists. However, improved survival of premature neonates coupled with a scarcity of available experts has raised concerns about the sustainability of this approach. We aimed to develop bespoke and code-free deep learning-based classifiers for plus disease, a hallmark of ROP, in an ethnically diverse population in London, UK, and externally validate them in ethnically, geographically, and socioeconomically diverse populations in four countries and three continents. Code-free deep learning is not reliant on the availability of expertly trained data scientists, thus being of particular potential benefit for low resource health-care settings. METHODS This retrospective cohort study used retinal images from 1370 neonates admitted to a neonatal unit at Homerton University Hospital NHS Foundation Trust, London, UK, between 2008 and 2018. Images were acquired using a Retcam Version 2 device (Natus Medical, Pleasanton, CA, USA) on all babies who were either born at less than 32 weeks gestational age or had a birthweight of less than 1501 g. Each images was graded by two junior ophthalmologists with disagreements adjudicated by a senior paediatric ophthalmologist. Bespoke and code-free deep learning models (CFDL) were developed for the discrimination of healthy, pre-plus disease, and plus disease. Performance was assessed internally on 200 images with the majority vote of three senior paediatric ophthalmologists as the reference standard. External validation was on 338 retinal images from four separate datasets from the USA, Brazil, and Egypt with images derived from Retcam and the 3nethra neo device (Forus Health, Bengaluru, India). FINDINGS Of the 7414 retinal images in the original dataset, 6141 images were used in the final development dataset. For the discrimination of healthy versus pre-plus or plus disease, the bespoke model had an area under the curve (AUC) of 0·986 (95% CI 0·973-0·996) and the CFDL model had an AUC of 0·989 (0·979-0·997) on the internal test set. Both models generalised well to external validation test sets acquired using the Retcam for discriminating healthy from pre-plus or plus disease (bespoke range was 0·975-1·000 and CFDL range was 0·969-0·995). The CFDL model was inferior to the bespoke model on discriminating pre-plus disease from healthy or plus disease in the USA dataset (CFDL 0·808 [95% CI 0·671-0·909, bespoke 0·942 [0·892-0·982]], p=0·0070). Performance also reduced when tested on the 3nethra neo imaging device (CFDL 0·865 [0·742-0·965] and bespoke 0·891 [0·783-0·977]). INTERPRETATION Both bespoke and CFDL models conferred similar performance to senior paediatric ophthalmologists for discriminating healthy retinal images from ones with features of pre-plus or plus disease; however, CFDL models might generalise less well when considering minority classes. Care should be taken when testing on data acquired using alternative imaging devices from that used for the development dataset. Our study justifies further validation of plus disease classifiers in ROP screening and supports a potential role for code-free approaches to help prevent blindness in vulnerable neonates. FUNDING National Institute for Health Research Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and the University College London Institute of Ophthalmology. TRANSLATIONS For the Portuguese and Arabic translations of the abstract see Supplementary Materials section.
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Affiliation(s)
- Siegfried K Wagner
- NIHR Moorfields Biomedical Research Centre, London, UK; Institute of Ophthalmology, University College London, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Bart Liefers
- NIHR Moorfields Biomedical Research Centre, London, UK
| | - Meera Radia
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Gongyu Zhang
- NIHR Moorfields Biomedical Research Centre, London, UK
| | - Robbert Struyven
- NIHR Moorfields Biomedical Research Centre, London, UK; Institute of Ophthalmology, University College London, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Livia Faes
- NIHR Moorfields Biomedical Research Centre, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Jonathan Than
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Shafi Balal
- Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | | | | | | | | | - Periklis Giannakis
- Institute of Health Sciences Education, Queen Mary University of London, London, UK
| | - Imoro Zeba Braimah
- Lions International Eye Centre, Korle-Bu Teaching Hospital, Accra, Ghana
| | - Islam S H Ahmed
- Faculty of Medicine, Alexandria University, Alexandria, Egypt; Alexandria University Hospital, Alexandria, Egypt
| | - Mariam Al-Feky
- Department of Ophthalmology, Ain Shams University Hospitals, Cairo, Egypt; Watany Eye Hospital, Cairo, Egypt
| | - Hagar Khalid
- Moorfields Eye Hospital NHS Foundation Trust, London, UK; Department of Ophthalmology, Tanta University, Tanta, Egypt
| | - Daniel Ferraz
- Institute of Ophthalmology, University College London, London, UK; D'Or Institute for Research and Education, São Paulo, Brazil
| | - Juliana Vieira
- Department of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Rodrigo Jorge
- Department of Ophthalmology, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
| | - Shahid Husain
- The Blizard Institute, Queen Mary University of London, London, UK; Neonatology Department, Homerton University Hospital NHS Foundation Trust, London, UK
| | - Janette Ravelo
- Neonatology Department, Homerton University Hospital NHS Foundation Trust, London, UK
| | | | - Robert Henderson
- UCL Great Ormond Street Institute of Child Health, University College London, London, UK; Clinical and Academic Department of Ophthalmology, Great Ormond Street Hospital for Children, London, UK
| | - Himanshu I Patel
- Moorfields Eye Hospital NHS Foundation Trust, London, UK; The Royal London Hospital, Barts Health NHS Trust, London, UK
| | - Susan Ostmo
- Department of Ophthalmology, Oregon Health & Science University, Portland, OR, USA
| | - J Peter Campbell
- Department of Ophthalmology, Oregon Health & Science University, Portland, OR, USA
| | - Nikolas Pontikos
- NIHR Moorfields Biomedical Research Centre, London, UK; Institute of Ophthalmology, University College London, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Praveen J Patel
- NIHR Moorfields Biomedical Research Centre, London, UK; Institute of Ophthalmology, University College London, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Pearse A Keane
- NIHR Moorfields Biomedical Research Centre, London, UK; Institute of Ophthalmology, University College London, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Gill Adams
- NIHR Moorfields Biomedical Research Centre, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Konstantinos Balaskas
- NIHR Moorfields Biomedical Research Centre, London, UK; Institute of Ophthalmology, University College London, London, UK; Moorfields Eye Hospital NHS Foundation Trust, London, UK.
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Wagner SK, Cortina-Borja M, Silverstein SM, Zhou Y, Romero-Bascones D, Struyven RR, Trucco E, Mookiah MRK, MacGillivray T, Hogg S, Liu T, Williamson DJ, Pontikos N, Patel PJ, Balaskas K, Alexander DC, Stuart KV, Khawaja AP, Denniston AK, Rahi JS, Petzold A, Keane PA. Association Between Retinal Features From Multimodal Imaging and Schizophrenia. JAMA Psychiatry 2023; 80:478-487. [PMID: 36947045 PMCID: PMC10034669 DOI: 10.1001/jamapsychiatry.2023.0171] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/23/2023] [Indexed: 03/23/2023]
Abstract
Importance The potential association of schizophrenia with distinct retinal changes is of clinical interest but has been challenging to investigate because of a lack of sufficiently large and detailed cohorts. Objective To investigate the association between retinal biomarkers from multimodal imaging (oculomics) and schizophrenia in a large real-world population. Design, Setting, and Participants This cross-sectional analysis used data from a retrospective cohort of 154 830 patients 40 years and older from the AlzEye study, which linked ophthalmic data with hospital admission data across England. Patients attended Moorfields Eye Hospital, a secondary care ophthalmic hospital with a principal central site, 4 district hubs, and 5 satellite clinics in and around London, United Kingdom, and had retinal imaging during the study period (January 2008 and April 2018). Data were analyzed from January 2022 to July 2022. Main Outcomes and Measures Retinovascular and optic nerve indices were computed from color fundus photography. Macular retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (mGC-IPL) thicknesses were extracted from optical coherence tomography. Linear mixed-effects models were used to examine the association between schizophrenia and retinal biomarkers. Results A total of 485 individuals (747 eyes) with schizophrenia (mean [SD] age, 64.9 years [12.2]; 258 [53.2%] female) and 100 931 individuals (165 400 eyes) without schizophrenia (mean age, 65.9 years [13.7]; 53 253 [52.8%] female) were included after images underwent quality control and potentially confounding conditions were excluded. Individuals with schizophrenia were more likely to have hypertension (407 [83.9%] vs 49 971 [48.0%]) and diabetes (364 [75.1%] vs 28 762 [27.6%]). The schizophrenia group had thinner mGC-IPL (-4.05 μm, 95% CI, -5.40 to -2.69; P = 5.4 × 10-9), which persisted when investigating only patients without diabetes (-3.99 μm; 95% CI, -6.67 to -1.30; P = .004) or just those 55 years and younger (-2.90 μm; 95% CI, -5.55 to -0.24; P = .03). On adjusted analysis, retinal fractal dimension among vascular variables was reduced in individuals with schizophrenia (-0.14 units; 95% CI, -0.22 to -0.05; P = .001), although this was not present when excluding patients with diabetes. Conclusions and Relevance In this study, patients with schizophrenia had measurable differences in neural and vascular integrity of the retina. Differences in retinal vasculature were mostly secondary to the higher prevalence of diabetes and hypertension in patients with schizophrenia. The role of retinal features as adjunct outcomes in patients with schizophrenia warrants further investigation.
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Affiliation(s)
- Siegfried K. Wagner
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Mario Cortina-Borja
- Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Steven M. Silverstein
- Department of Psychiatry, University of Rochester Medical Center, Rochester, New York
- Department of Ophthalmology, University of Rochester Medical Center, Rochester, New York
- Department of Neuroscience, University of Rochester Medical Center, Rochester, New York
- Center for Visual Science, University of Rochester, Rochester, New York
| | - Yukun Zhou
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - David Romero-Bascones
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Mondragón, Spain
| | - Robbert R. Struyven
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Emanuele Trucco
- VAMPIRE Project, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Muthu R. K. Mookiah
- VAMPIRE Project, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Tom MacGillivray
- VAMPIRE Project, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen Hogg
- VAMPIRE Project, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Timing Liu
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Dominic J. Williamson
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Nikolas Pontikos
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Praveen J. Patel
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Konstantinos Balaskas
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Kelsey V. Stuart
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Anthony P. Khawaja
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Alastair K. Denniston
- University of Birmingham, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, United Kingdom
| | - Jugnoo S. Rahi
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
- Great Ormond Street Hospital NHS Foundation Trust, London, United Kingdom
- Ulverscroft Vision Research Group, University College London, London, United Kingdom
- NIHR Biomedical Research Centre at UCL Great Ormond Street Institute of Child Health and Great Ormond Street Hospital, London, United Kingdom
| | - Axel Petzold
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Pearse A. Keane
- NIHR Moorfields Biomedical Research Centre, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
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Fu DJ, Hanumunthadu D, Keenan TDL, Wagner S, Balsakas K, Keane PA, Patel PJ. Characterising treatment outcomes of patients achieving quarterly aflibercept dosing for neovascular age-related macular degeneration: real-world clinical outcomes from a large tertiary care centre. Eye (Lond) 2023; 37:779-784. [PMID: 36085360 PMCID: PMC9998641 DOI: 10.1038/s41433-022-02220-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Revised: 06/09/2022] [Accepted: 08/16/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND AND OBJECTIVE To evaluate the proportion of patients achieving a 12-week (q12) aflibercept dosing interval in patients with neovascular age-related macular degeneration (nAMD). PATIENTS AND METHODS Retrospective, comparative, non-randomised electronic medical record (EMR) database study of the Moorfields database of treatment-naïve nAMD eyes. Extraction criteria included at least 7 aflibercept injections in first year of treatment, AMD in the diagnosis field of EMR, and minimum of 1 year follow-up data. RESULTS There were 2416 eyes of 2163 patients started on anti-vascular endothelial growth factor (anti-VEGF) between 01-11-2013 & 14-02-2020 who had received at least 7 aflibercept intravitreal injections (electronic database accessed March 2021). Of these, 1674 (68%) eyes of 1537 patients had at least one q12 dosing interval (>=84 and < =98 days between injections) during the first 2 years of treatment. This included 926 (61.8%) female patients and 856 (right eyes age at 1st injection), 936 (62.4%) Caucasian, and 32 (2.1%) Afro-Caribbean patients. The median time to the first q12 injection (95% confidence interval) was 1.76 years (1.70-1.86) with mean (±SD) of 11.8 (±6.0) injections. Visual acuity (ETDRS letters) of the eyes without q12 injection and eyes with a q12 injection was 57.9 ± 14.7 and 56.7 ± 14.8 respectively at baseline, 61.4 ± 18.1 and 63.0 ± 15.9 respectively at 12 months and 61.2 ± 20.1 and 61.1 ± 17.8 respectively at 24 months. CONCLUSION 68% of eyes were able to achieve a q12 injection dose within the first 2 years of treatment. Eyes achieving a q12 injection in the first 2 years achieved a similar visual acuity outcome at both 1 and 2-year follow-up to those unable to do so, with a fewer number of total injections.
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Affiliation(s)
- Dun Jack Fu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Daren Hanumunthadu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Royal Free London NHS Foundation Trust, London, UK
| | - Tiarnan D L Keenan
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Siegfried Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Konstantinos Balsakas
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Praveen J Patel
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
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Hanumunthadu D, Saleh A, Florea D, Balaskas K, Keane PA, Aslam T, Patel PJ. Biomarkers of macular neovascularisation activity using optical coherence tomography angiography in treated stable neovascular age related macular degeneration. BMC Ophthalmol 2023; 23:68. [PMID: 36782163 PMCID: PMC9926859 DOI: 10.1186/s12886-022-02749-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2022] [Accepted: 12/19/2022] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND The aim of this study was to describe features of disease activity in patients with treated stable macular neovascularisation (MNV) in neovascular age related macular degeneration (nAMD) using optical coherence tomography angiography (OCTA). METHODS Thirty-two eyes of 32 patients with nAMD were included in this prospective, observational study. These patients were undergoing treatment with aflibercept on a treat-and-extend regimen attending an extension to a 12-week treatment interval. RESULTS All subjects had no macular haemorrhage and no structural OCT markers of active MNV activity at the index 12-week treatment extension visit. 31/32 OCTA images were gradeable without significant imaging artefact. The mean MNV size was 3.6mm2 ± 4.6mm2 and 27 (87.1%) had detectable MNV blood flow. 29/31 (93.5%) subjects had MNV with mature phenotypes including 10 non-specific, 10 tangle and 3 deadtree phenotypes. MNV halo and MNV central feeder vessel were noted in 18 (58.1%) and 19 (61.3%) of subjects respectively; only 1 (3.2%) subject was noted to have a MNV capillary fringe. CONCLUSIONS MNV blood flow is still detectable using OCTA in the majority of subjects in this study with treated stable MNV. OCTA features associated included MNV mature phenotype, MNV feeder vessel, MNV halo and absence of capillary fringe.
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Affiliation(s)
- Daren Hanumunthadu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.,Royal Free London NHS Foundation Trust, London, UK
| | - Azahir Saleh
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Daniela Florea
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Tariq Aslam
- Manchester Royal Eye Hospital, NHS Central Manchester University Hospitals and University of Manchester, Manchester, UK
| | - Praveen J Patel
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
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Taribagil P, Hogg HDJ, Balaskas K, Keane PA. Integrating artificial intelligence into an ophthalmologist’s workflow: obstacles and opportunities. Expert Review of Ophthalmology 2023. [DOI: 10.1080/17469899.2023.2175672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Priyal Taribagil
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - HD Jeffry Hogg
- Medical Retina Department, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Department of Population Health Science, Population Health Science Institute, Newcastle University, Newcastle upon Tyne, UK
- Department of Ophthalmology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Freeman Road, Newcastle upon Tyne, UK
| | - Konstantinos Balaskas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Medical Retina, Institute of Ophthalmology, University College of London Institute of Ophthalmology, London, UK
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Bowness JS, Burckett-St Laurent D, Hernandez N, Keane PA, Lobo C, Margetts S, Moka E, Pawa A, Rosenblatt M, Sleep N, Taylor A, Woodworth G, Vasalauskaite A, Noble JA, Higham H. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth 2023; 130:217-225. [PMID: 35987706 PMCID: PMC9900723 DOI: 10.1016/j.bja.2022.06.031] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/01/2022] [Accepted: 06/27/2022] [Indexed: 02/09/2023] Open
Abstract
BACKGROUND Ultrasonound is used to identify anatomical structures during regional anaesthesia and to guide needle insertion and injection of local anaesthetic. ScanNav Anatomy Peripheral Nerve Block (Intelligent Ultrasound, Cardiff, UK) is an artificial intelligence-based device that produces a colour overlay on real-time B-mode ultrasound to highlight anatomical structures of interest. We evaluated the accuracy of the artificial-intelligence colour overlay and its perceived influence on risk of adverse events or block failure. METHODS Ultrasound-guided regional anaesthesia experts acquired 720 videos from 40 volunteers (across nine anatomical regions) without using the device. The artificial-intelligence colour overlay was subsequently applied. Three more experts independently reviewed each video (with the original unmodified video) to assess accuracy of the colour overlay in relation to key anatomical structures (true positive/negative and false positive/negative) and the potential for highlighting to modify perceived risk of adverse events (needle trauma to nerves, arteries, pleura, and peritoneum) or block failure. RESULTS The artificial-intelligence models identified the structure of interest in 93.5% of cases (1519/1624), with a false-negative rate of 3.0% (48/1624) and a false-positive rate of 3.5% (57/1624). Highlighting was judged to reduce the risk of unwanted needle trauma to nerves, arteries, pleura, and peritoneum in 62.9-86.4% of cases (302/480 to 345/400), and to increase the risk in 0.0-1.7% (0/160 to 8/480). Risk of block failure was reported to be reduced in 81.3% of scans (585/720) and to be increased in 1.8% (13/720). CONCLUSIONS Artificial intelligence-based devices can potentially aid image acquisition and interpretation in ultrasound-guided regional anaesthesia. Further studies are necessary to demonstrate their effectiveness in supporting training and clinical practice. CLINICAL TRIAL REGISTRATION NCT04906018.
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Affiliation(s)
- James S Bowness
- Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Aneurin Bevan University Health Board, Newport, UK.
| | | | - Nadia Hernandez
- Department of Anesthesiology, Memorial Hermann Hospital, Texas Medical Centre, Houston, TX, USA
| | - Pearse A Keane
- Institute of Ophthalmology, Faculty of Brain Sciences, University College London, London, UK; National Institute for Health and Care Research Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Clara Lobo
- Anesthesiology Institute, Cleveland Clinic Abu Dhabi, Abu Dhabi, United Arab Emirates
| | | | - Eleni Moka
- Anaesthesiology Department, Creta InterClinic Hospital, Hellenic Healthcare Group, Heraklion, Crete, Greece
| | - Amit Pawa
- Department of Anaesthesia, Guy's and St Thomas' Hospitals NHS Trust, London, UK; Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Meg Rosenblatt
- Department of Anesthesiology, Perioperative and Pain Medicine, Mount Sinai Morningside and West Hospitals, New York, NY, USA
| | | | | | - Glenn Woodworth
- Department of Anesthesiology and Perioperative Medicine, Oregon Health & Science University, Portland, OR, USA
| | | | - J Alison Noble
- Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Helen Higham
- Oxford Simulation, Teaching and Research Centre, University of Oxford, Oxford, UK; Department of Anaesthesia, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
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Mollan SP, Fu DJ, Chuo CY, Gannon JG, Lee WH, Hopkins JJ, Hughes C, Denniston AK, Keane PA, Cantrell R. Predicting the immediate impact of national lockdown on neovascular age-related macular degeneration and associated visual morbidity: an INSIGHT Health Data Research Hub for Eye Health report. Br J Ophthalmol 2023; 107:267-274. [PMID: 34518162 PMCID: PMC9887382 DOI: 10.1136/bjophthalmol-2021-319383] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 08/30/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Predicting the impact of neovascular age-related macular degeneration (nAMD) service disruption on visual outcomes following national lockdown in the UK to contain SARS-CoV-2. METHODS AND ANALYSIS This retrospective cohort study includes deidentified data from 2229 UK patients from the INSIGHT Health Data Research digital hub. We forecasted the number of treatment-naïve nAMD patients requiring anti-vascular endothelial growth factor (anti-VEGF) initiation during UK lockdown (16 March 2020 through 31 July 2020) at Moorfields Eye Hospital (MEH) and University Hospitals Birmingham (UHB). Best-measured visual acuity (VA) changes without anti-VEGF therapy were predicted using post hoc analysis of Minimally Classic/Occult Trial of the Anti-VEGF Antibody Ranibizumab in the Treatment of Neovascular AMD trial sham-control arm data (n=238). RESULTS At our centres, 376 patients were predicted to require anti-VEGF initiation during lockdown (MEH: 325; UHB: 51). Without treatment, mean VA was projected to decline after 12 months. The proportion of eyes in the MEH cohort predicted to maintain the key positive visual outcome of ≥70 ETDRS letters (Snellen equivalent 6/12) fell from 25.5% at baseline to 5.8% at 12 months (UHB: 9.8%-7.8%). Similarly, eyes with VA <25 ETDRS letters (6/96) were predicted to increase from 4.3% to 14.2% at MEH (UHB: 5.9%-7.8%) after 12 months without treatment. CONCLUSIONS Here, we demonstrate how combining data from a recently founded national digital health data repository with historical industry-funded clinical trial data can enhance predictive modelling in nAMD. The demonstrated detrimental effects of prolonged treatment delay should incentivise healthcare providers to support nAMD patients accessing care in safe environments. TRIAL REGISTRATION NUMBER NCT00056836.
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Affiliation(s)
- Susan P Mollan
- Ophthalmology, University Hospitals Birmingham, Birmingham, UK
| | - Dun Jack Fu
- Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | | | | | - Wen Hwa Lee
- Action Against Age-Related Macular Degeneration, Oxford, UK
| | | | | | - Alastair K Denniston
- Department of Ophthalmology, University Hospitals Birmingham NHSFT, Birmingham, UK
| | - Pearse A Keane
- Medical Retina, Moorfields Eye Hospital NHS Foundation Trust, London, UK
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Hogg HDJ, Al-Zubaidy M, Talks J, Denniston AK, Kelly CJ, Malawana J, Papoutsi C, Teare MD, Keane PA, Beyer FR, Maniatopoulos G. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. J Med Internet Res 2023; 25:e39742. [PMID: 36626192 PMCID: PMC9875023 DOI: 10.2196/39742] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/28/2022] [Accepted: 11/30/2022] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. OBJECTIVE In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. METHODS Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals' perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning-enabled or non-rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. RESULTS The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non-rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. CONCLUSIONS Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non-rule-based clinical AI implementation. TRIAL REGISTRATION PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/33145.
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Affiliation(s)
- Henry David Jeffry Hogg
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Mohaimen Al-Zubaidy
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - James Talks
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Alastair K Denniston
- Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | | | - Johann Malawana
- The Healthcare Leadership Academy, London, United Kingdom
- The Institute of Leadership and Management, Birmingham, United Kingdom
| | - Chrysanthi Papoutsi
- Nuffield Department of Primary Healthcare Sciences, Oxford University, Oxford, United Kingdom
| | - Marion Dawn Teare
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Pearse A Keane
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
- Institute of Ophthalmology, University College London, London, United Kingdom
| | - Fiona R Beyer
- Evidence Synthesis Group, Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Gregory Maniatopoulos
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Faculty of Business and Law, Northumbria University, Newcastle upon Tyne, United Kingdom
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Tint NL, Cheng KKW, Dhillon AS, Keane PA, Alexander P, Kennedy D, Chau DYS, Rose FRAJ, Allan BDS. An in vitro assessment of the thermoreversible PLGA-PEG-PLGA copolymer: Implications for Descemet's membrane endothelial keratoplasty. Clin Exp Ophthalmol 2023; 51:58-66. [PMID: 36086942 DOI: 10.1111/ceo.14167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 08/29/2022] [Accepted: 09/02/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND To explore the use of a thermoreversible copolymer gel coating to prevent donor tissue scrolling in Descemet's membrane endothelial keratoplasty (DMEK). METHODS PLGA-PEG-PLGA triblock copolymer was synthesised via ring opening polymerisation. Two formulations were fabricated and gelation properties characterised using rheological analyses. Endothelial cytotoxicity of the copolymer was assessed using a Trypan Blue exclusion assay. Thickness of the copolymer gel coating on the endothelial surface was analysed using anterior segment optical coherence tomography (OCT) (RTVue-100, Optovue Inc.). Gold nanoparticles were added to the copolymer to aid visualisation using OCT. Prevention of Descemet membrane donor scrolling was represented via a novel, in vitro, immersion of copolymer coated donor graft material. RESULTS Two different formulations of PLGA-PEG-PLGA copolymer were successfully fabricated and the desired peak gelling temperature of 24°C was achieved by polymer blending. Application of 20%, 30% and 40% (wt/vol) polymer concentrations resulted in a statistically significant increase in polymer thickness on the endothelium (p < 0.001). There was no detectable endothelial cytotoxicity. The polymer was easy to apply to the endothelium and prevented scrolling of the DMEK graft. CONCLUSION This PLGA-PEG-PLGA thermoreversible copolymer gel could be exploited as a therapeutic aid for preventing DMEK graft scrolling.
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Affiliation(s)
- Naing L Tint
- Moorfields Eye Hospital NHS Foundation Trust, Cornea and External Eye Disease Service, London, UK.,Princess Alexandra Eye Pavilion, NHS Lothian, Edinburgh, UK
| | | | - Amritpaul S Dhillon
- Division of Regenerative Medicine and Cellular Therapies, Nottingham Biodiscovery Institute, School of Pharmacy Nottingham, University of Nottingham, Nottinghamshire, UK
| | - Pearse A Keane
- Institute of Ophthalmology, University College London, London, UK.,Moorfields Eye Hospital NHS Foundation Trust, NIHR Biomedical Research Centre for Ophthalmology, London, London, UK
| | - Philip Alexander
- Division of Regenerative Medicine and Cellular Therapies, Nottingham Biodiscovery Institute, School of Pharmacy Nottingham, University of Nottingham, Nottinghamshire, UK
| | - David Kennedy
- Moorfields Eye Hospital NHS Foundation Trust, Lions Eye Bank, London, UK
| | - David Y S Chau
- Division of Biomaterials and Tissue Engineering, Eastman Dental Institute, University College London, London, London, UK
| | - Felicity R A J Rose
- Division of Regenerative Medicine and Cellular Therapies, Nottingham Biodiscovery Institute, School of Pharmacy Nottingham, University of Nottingham, Nottinghamshire, UK
| | - Bruce D S Allan
- Moorfields Eye Hospital NHS Foundation Trust, Cornea and External Eye Disease Service, London, UK.,Moorfields Eye Hospital NHS Foundation Trust, NIHR Biomedical Research Centre for Ophthalmology, London, London, UK
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Chia MA, Taylor JR, Stuart KV, Khawaja AP, Foster PJ, Keane PA, Turner AW. Prevalence of Diabetic Retinopathy in Indigenous and Non-Indigenous Australians: A Systematic Review and Meta-analysis. Ophthalmology 2023; 130:56-67. [PMID: 35931223 DOI: 10.1016/j.ophtha.2022.07.024] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 01/06/2023] Open
Abstract
TOPIC This systematic review and meta-analysis summarizes evidence relating to the prevalence of diabetic retinopathy (DR) among Indigenous and non-Indigenous Australians. CLINICAL RELEVANCE Indigenous Australians suffer disproportionately from diabetes-related complications. Exploring ethnic variation in disease is important for equitable distribution of resources and may lead to identification of ethnic-specific modifiable risk factors. Existing DR prevalence studies comparing Indigenous and non-Indigenous Australians have shown conflicting results. METHODS This study was conducted following Joanna Briggs Institute guidance on systematic reviews of prevalence studies (PROSPERO ID: CRD42022259048). We performed searches of Medline (Ovid), EMBASE, and Web of Science until October 2021, using a strategy designed by an information specialist. We included studies reporting DR prevalence among diabetic patients in Indigenous and non-Indigenous Australian populations. Two independent reviewers performed quality assessments using a 9-item appraisal tool. Meta-analysis and meta-regression were performed using double arcsine transformation and a random-effects model comparing Indigenous and non-Indigenous subgroups. RESULTS Fifteen studies with 8219 participants met criteria for inclusion. The Indigenous subgroup scored lower on the appraisal tool than the non-Indigenous subgroup (mean score 50% vs. 72%, P = 0.04). In the unadjusted meta-analysis, DR prevalence in the Indigenous subgroup (30.2%; 95% confidence interval [CI], 24.9-35.7) did not differ significantly (P = 0.17) from the non-Indigenous subgroup (23.7%; 95% CI, 16.8-31.4). After adjusting for age and quality, DR prevalence was higher in the Indigenous subgroup (P < 0.01), with prevalence ratio point estimates ranging from 1.72 to 2.58, depending on the meta-regression model. For the secondary outcomes, prevalence estimates were higher in the Indigenous subgroup for diabetic macular edema (DME) (8.7% vs. 2.7%, P = 0.02) and vision-threatening DR (VTDR) (8.6% vs. 3.0%, P = 0.03) but not for proliferative DR (2.5% vs. 0.8%, P = 0.07). CONCLUSIONS Indigenous studies scored lower for methodological quality, raising the possibility that systematic differences in research practices may be leading to underestimation of disease burden. After adjusting for age and quality, we found a higher DR prevalence in the Indigenous subgroup. This contrasts with a previous review that reported the opposite finding of lower DR prevalence using unadjusted pooled estimates. Future epidemiological work exploring DR burden in Indigenous communities should aim to address methodological weaknesses identified by this review.
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Affiliation(s)
- Mark A Chia
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom; Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia.
| | - Joshua R Taylor
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia
| | - Kelsey V Stuart
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Paul J Foster
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Angus W Turner
- Lions Outback Vision, Lions Eye Institute, Nedlands, Western Australia, Australia; University of Western Australia, Perth, Western Australia, Australia
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47
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Khan H, Amjad R, Keane PA, Denniston AK, Lujan BJ. Central posterior hyaloidal fibrosis - A novel optical coherence tomography feature associated with choroidal neovascular membrane. Am J Ophthalmol Case Rep 2022; 28:101709. [PMID: 36177297 PMCID: PMC9513726 DOI: 10.1016/j.ajoc.2022.101709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/25/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose To describe a novel optical coherence tomography (OCT) finding at the vitreomacular interface (VMI), and report its association with advanced choroidal neovascularisation (CNV). Observations Optical coherence tomography (OCT) scans performed at three retinal imaging centres at Amanat Eye Hospital, Pakistan from May 2016 till May 2021 were reviewed. A specific change at the vitreomacular interface was noted consisting of abnormal hyper reflectivity at the point of attachment of the posterior hyaloid membrane to the foveal center which appears to 'fill in' the foveolar depression.Eight eyes of eight patients were identified. All affected eyes had advanced CNV and persistent vitreofoveolar adhesion. In all eyes, the foveal contour (concavity) was maintained and there was no inner retinal surface wrinkling which differentiates this OCT feature from vitreomacular traction or epiretinal membranes. The authors propose the term Central Posterior Hyaloidal Fibrosis (CPHF) for this specific OCT finding. Conclusions and Importance Central Posterior Hyaloidal Fibrosis (CPHF) is a newly reported OCT finding associated with advanced CNV, which may represent a possible profibrotic influence of a choroidal neovascular membrane to the overlying posterior hyaloid adhesion.
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Affiliation(s)
- Hina Khan
- Amanat Eye Hospital, Islamabad, Pakistan
| | - Rida Amjad
- Amanat Eye Hospital, Islamabad, Pakistan
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom
| | - Alastair K Denniston
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London, United Kingdom.,Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
| | - Brandon J Lujan
- Casey Eye Institute, Oregon Health & Science University, Portland, USA
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48
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Balaskas K, Glinton S, Keenan TDL, Faes L, Liefers B, Zhang G, Pontikos N, Struyven R, Wagner SK, McKeown A, Patel PJ, Keane PA, Fu DJ. Prediction of visual function from automatically quantified optical coherence tomography biomarkers in patients with geographic atrophy using machine learning. Sci Rep 2022; 12:15565. [PMID: 36114218 PMCID: PMC9481631 DOI: 10.1038/s41598-022-19413-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022] Open
Abstract
Geographic atrophy (GA) is a vision-threatening manifestation of age-related macular degeneration (AMD), one of the leading causes of blindness globally. Objective, rapid, reliable, and scalable quantification of GA from optical coherence tomography (OCT) retinal scans is necessary for disease monitoring, prognostic research, and clinical endpoints for therapy development. Such automatically quantified biomarkers on OCT are likely to further elucidate structure-function correlation in GA and thus the pathophysiological mechanisms of disease development and progression. In this work, we aimed to predict visual function with machine-learning applied to automatically acquired quantitative imaging biomarkers in GA. A post-hoc analysis of data from a clinical trial and routine clinical care was conducted. A deep-learning automated segmentation model was applied on OCT scans from 476 eyes (325 patients) with GA. A separate machine learning prediction model (Random Forest) used the resultant quantitative OCT (qOCT) biomarkers to predict cross-sectional visual acuity under standard (VA) and low luminance (LLVA). The primary outcome was regression coefficient (r2) and mean absolute error (MAE) for cross-sectional VA and LLVA in Early Treatment Diabetic Retinopathy Study (ETDRS) letters. OCT parameters were predictive of VA (r2 0.40 MAE 11.7 ETDRS letters) and LLVA (r2 0.25 MAE 12.1). Normalised random forest feature importance, as a measure of the predictive value of the three constituent features of GA; retinal pigment epithelium (RPE)-loss, photoreceptor degeneration (PDR), hypertransmission and their locations, was reported both on voxel-level heatmaps and ETDRS-grid subfields. The foveal region (46.5%) and RPE-loss (31.1%) had greatest predictive importance for VA. For LLVA, however, non-foveal regions (74.5%) and PDR (38.9%) were most important. In conclusion, automated qOCT biomarkers demonstrate predictive significance for VA and LLVA in GA. LLVA is itself predictive of GA progression, implying that the predictive qOCT biomarkers provided by our model are also prognostic.
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Affiliation(s)
- Konstantinos Balaskas
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK.
| | - S Glinton
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - T D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
| | - L Faes
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - B Liefers
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - G Zhang
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - N Pontikos
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - R Struyven
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - S K Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - A McKeown
- Apellis Pharmaceuticals, Inc, Waltham, MA, USA
| | - P J Patel
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - P A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
| | - D J Fu
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, Moorfields Reading Centre and Clinical AI Hub, 162 City Rd, London, EC1V 2PD, UK
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Subramanian A, Han D, Braithwaite T, Thayakaran R, Zemedikun DT, Gokhale KM, Lee WH, Coker J, Keane PA, Denniston AK, Nirantharakumar K, Azoulay L, Adderley NJ. Angiotensin-converting enzyme inhibitors and risk of age-related macular degeneration in individuals with hypertension. Br J Clin Pharmacol 2022; 88:4199-4210. [PMID: 35474585 PMCID: PMC9541840 DOI: 10.1111/bcp.15366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/19/2022] [Accepted: 04/04/2022] [Indexed: 11/27/2022] Open
Abstract
AIMS Several observational studies have examined the potential protective effect of angiotensin-converting enzyme inhibitor (ACE-I) use on the risk of age-related macular degeneration (AMD) and have reported contradictory results owing to confounding and time-related biases. We aimed to assess the risk of AMD in a base cohort of patients aged 40 years and above with hypertension among new users of ACE-I compared to an active comparator cohort of new users of calcium channel blockers (CCB) using data obtained from IQVIA Medical Research Data, a primary care database in the UK. METHODS In this study, 53 832 and 43 106 new users of ACE-I and CCB were included between 1995 and 2019, respectively. In an on-treatment analysis, patients were followed up from the time of index drug initiation to the date of AMD diagnosis, loss to follow-up, discontinuation or switch to the comparator drug. A comprehensive range of covariates were used to estimate propensity scores to weight and match new users of ACE-I and CCB. Standardized mortality ratio weighted Cox proportional hazards model was used to estimate hazard ratios of developing AMD. RESULTS During a median follow-up of 2 years (interquartile range 1-5 years), the incidence rate of AMD was 2.4 (95% confidence interval 2.2-2.6) and 2.2 (2.0-2.4) per 1000 person-years among the weighted new users of ACE-I and CCB, respectively. There was no association of ACE-I use on the risk of AMD compared to CCB use in either the propensity score weighted or matched, on-treatment analysis (adjusted hazard ratio: 1.07 [95% confidence interval 0.90-1.27] and 0.87 [0.71-1.07], respectively). CONCLUSION We found no evidence that the use of ACE-I is associated with risk of AMD in patients with hypertension.
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Affiliation(s)
| | - Diana Han
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Tasanee Braithwaite
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.,The School of Immunology and Microbial Sciences and The School of Life Course Sciences, Kings College London, London, UK.,The Medical Eye Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Rasiah Thayakaran
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Dawit T Zemedikun
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Krishna M Gokhale
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Wen Hwa Lee
- Action Against Age-Related Macular Degeneration, London, UK
| | - Jesse Coker
- Action Against Age-Related Macular Degeneration, London, UK
| | - Pearse A Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK
| | - Alastair K Denniston
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and Institute of Ophthalmology, University College London, London, UK.,University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK.,Health Data Research UK (HDRUK), London, UK.,Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Krishnarajah Nirantharakumar
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK.,Health Data Research UK (HDRUK), London, UK
| | - Laurent Azoulay
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.,Centre for Clinical Epidemiology, Lady Davis Institute, Montreal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
| | - Nicola J Adderley
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
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50
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Denniston AK, Kale AU, Lee WH, Mollan SP, Keane PA. Building trust in real-world data: lessons from INSIGHT, the UK's health data research hub for eye health and oculomics. Curr Opin Ophthalmol 2022; 33:399-406. [PMID: 35916569 DOI: 10.1097/icu.0000000000000887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW In this review, we consider the challenges of creating a trusted resource for real-world data in ophthalmology, based on our experience of establishing INSIGHT, the UK's Health Data Research Hub for Eye Health and Oculomics. RECENT FINDINGS The INSIGHT Health Data Research Hub maximizes the benefits and impact of historical, patient-level UK National Health Service (NHS) electronic health record data, including images, through making it research-ready including curation and anonymisation. It is built around a shared 'north star' of enabling research for patient benefit. INSIGHT has worked to establish patient and public trust in the concept and delivery of INSIGHT, with efficient and robust governance processes that support safe and secure access to data for researchers. By linking to systemic data, there is an opportunity for discovery of novel ophthalmic biomarkers of systemic diseases ('oculomics'). Datasets that provide a representation of the whole population are an important tool to address the increasingly recognized threat of health data poverty. SUMMARY Enabling efficient, safe access to routinely collected clinical data is a substantial undertaking, especially when this includes imaging modalities, but provides an exceptional resource for research. Research and innovation built on inclusive real-world data is an important tool in ensuring that discoveries and technologies of the future may not only favour selected groups, but also work for all patients.
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Affiliation(s)
- Alastair K Denniston
- INSIGHT Health Data Research hub for Eye Health
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham
- Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham
| | - Aditya U Kale
- INSIGHT Health Data Research hub for Eye Health
- Academic Unit of Ophthalmology, Institute of Inflammation & Ageing, College of Medical and Dental Sciences, University of Birmingham
| | - Wen Hwa Lee
- INSIGHT Health Data Research hub for Eye Health
- Action Against Age-Related Macular Degeneration, London
| | - Susan P Mollan
- INSIGHT Health Data Research hub for Eye Health
- Ophthalmology Department, University Hospitals Birmingham NHS Foundation Trust, Birmingham
- Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham
| | - Pearse A Keane
- INSIGHT Health Data Research hub for Eye Health
- NIHR Biomedical Research Centre At Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, UK
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