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Daich Varela M, Sen S, De Guimaraes TAC, Kabiri N, Pontikos N, Balaskas K, Michaelides M. Artificial intelligence in retinal disease: clinical application, challenges, and future directions. Graefes Arch Clin Exp Ophthalmol 2023; 261:3283-3297. [PMID: 37160501 PMCID: PMC10169139 DOI: 10.1007/s00417-023-06052-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/20/2023] [Accepted: 03/24/2023] [Indexed: 05/11/2023] Open
Abstract
Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.
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Affiliation(s)
- Malena Daich Varela
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | | | | | - Nikolas Pontikos
- UCL Institute of Ophthalmology, London, UK
- Moorfields Eye Hospital, London, UK
| | | | - Michel Michaelides
- UCL Institute of Ophthalmology, London, UK.
- Moorfields Eye Hospital, London, UK.
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Sen P, Bamel A. Commentary: Deep learning in retinopathy of prematurity: Where do we stand? Indian J Ophthalmol 2022; 70:1279. [PMID: 35326033 PMCID: PMC9240484 DOI: 10.4103/ijo.ijo_3036_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Affiliation(s)
- Parveen Sen
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Arjun Bamel
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
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Google’s new AI technology detects cardiac issues using retinal scan. APPLIED NANOSCIENCE 2022. [DOI: 10.1007/s13204-021-02208-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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4
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Tan Z, Zhu Z, He Z, He M. Artificial Intelligence in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Arnould L, Guenancia C, Bourredjem A, Binquet C, Gabrielle PH, Eid P, Baudin F, Kawasaki R, Cottin Y, Creuzot-Garcher C, Jacquir S. Prediction of Cardiovascular Parameters With Supervised Machine Learning From Singapore "I" Vessel Assessment and OCT-Angiography: A Pilot Study. Transl Vis Sci Technol 2021; 10:20. [PMID: 34767626 PMCID: PMC8590163 DOI: 10.1167/tvst.10.13.20] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Purpose Assessment of cardiovascular risk is the keystone of prevention in cardiovascular disease. The objective of this pilot study was to estimate the cardiovascular risk score (American Hospital Association [AHA] risk score, Syntax risk, and SCORE risk score) with machine learning (ML) model based on retinal vascular quantitative parameters. Methods We proposed supervised ML algorithm to predict cardiovascular parameters in patients with cardiovascular diseases treated in Dijon University Hospital using quantitative retinal vascular characteristics measured with fundus photography and optical coherence tomography – angiography (OCT-A) scans (alone and combined). To describe retinal microvascular network, we used the Singapore “I” Vessel Assessment (SIVA), which extracts vessel parameters from fundus photography and quantitative OCT-A retinal metrics of superficial retinal capillary plexus. Results The retinal and cardiovascular data of 144 patients were included. This paper presented a high prediction rate of the cardiovascular risk score. By means of the Naïve Bayes algorithm and SIVA + OCT-A data, the AHA risk score was predicted with 81.25% accuracy, the SCORE risk with 75.64% accuracy, and the Syntax score with 96.53% of accuracy. Conclusions Performance of these algorithms demonstrated in this preliminary study that ML algorithms applied to quantitative retinal vascular parameters with SIVA software and OCT-A were able to predict cardiovascular scores with a robust rate. Quantitative retinal vascular biomarkers with the ML strategy might provide valuable data to implement predictive model for cardiovascular parameters. Translational Relevance Small data set of quantitative retinal vascular parameters with fundus and with OCT-A can be used with ML learning to predict cardiovascular parameters.
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Affiliation(s)
- Louis Arnould
- Ophthalmology Department, University Hospital, Dijon, France.,INSERM, CIC1432, Clinical Epidemiology Unit, Dijon, France; Dijon University Hospital, Clinical Investigation Center, Clinical Epidemiology/Clinical Trials Unit, Dijon, France.,Centre des Sciences du Gout et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Charles Guenancia
- Cardiology Department, University Hospital, Dijon, France.,PEC 2, University Hospital, Dijon, France
| | - Abderrahmane Bourredjem
- INSERM, CIC1432, Clinical Epidemiology Unit, Dijon, France; Dijon University Hospital, Clinical Investigation Center, Clinical Epidemiology/Clinical Trials Unit, Dijon, France
| | - Christine Binquet
- INSERM, CIC1432, Clinical Epidemiology Unit, Dijon, France; Dijon University Hospital, Clinical Investigation Center, Clinical Epidemiology/Clinical Trials Unit, Dijon, France
| | - Pierre-Henry Gabrielle
- Ophthalmology Department, University Hospital, Dijon, France.,Centre des Sciences du Gout et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Pétra Eid
- Ophthalmology Department, University Hospital, Dijon, France
| | - Florian Baudin
- Ophthalmology Department, University Hospital, Dijon, France
| | - Ryo Kawasaki
- Department of Vision Informatics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Yves Cottin
- Cardiology Department, University Hospital, Dijon, France.,PEC 2, University Hospital, Dijon, France
| | - Catherine Creuzot-Garcher
- Ophthalmology Department, University Hospital, Dijon, France.,Centre des Sciences du Gout et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Sabir Jacquir
- Université Paris-Saclay, CNRS, Institut des Neurosciences Paris-Saclay, Gif-sur-Yvette, France
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Abstract
BACKGROUND Cerebrovascular disease (CeVD), including stroke, is a leading cause of death globally. The retina is an extension of the cerebrum, sharing embryological and vascular pathways. The association between different retinal signs and CeVD has been extensively evaluated. In this review, we summarize recent studies which have examined this association. EVIDENCE ACQUISITION We searched 6 databases through July 2019 for studies evaluating the link between retinal vascular signs and diseases with CeVD. CeVD was classified into 2 groups: clinical CeVD (including clinical stroke, silent cerebral infarction, cerebral hemorrhage, and stroke mortality), and sub-clinical CeVD (including MRI-defined lacunar infarct and white matter lesions [WMLs]). Retinal vascular signs were classified into 3 groups: classic hypertensive retinopathy (including retinal microaneurysms, retinal microhemorrhage, focal/generalized arteriolar narrowing, cotton-wool spots, and arteriovenous nicking), clinical retinal diseases (including diabetic retinopathy [DR], age-related macular degeneration [AMD], retinal vein occlusion, retinal artery occlusion [RAO], and retinal emboli), and retinal vascular imaging measures (including retinal vessel diameter and geometry). We also examined emerging retinal vascular imaging measures and the use of artificial intelligence (AI) deep learning (DL) techniques. RESULTS Hypertensive retinopathy signs were consistently associated with clinical CeVD and subclinical CeVD subtypes including subclinical cerebral large artery infarction, lacunar infarction, and WMLs. Some clinical retinal diseases such as DR, retinal arterial and venous occlusion, and transient monocular vision loss are consistently associated with clinical CeVD. There is an increased risk of recurrent stroke immediately after RAO. Less consistent associations are seen with AMD. Retinal vascular imaging using computer assisted, semi-automated software to measure retinal vascular caliber and other parameters (tortuosity, fractal dimension, and branching angle) has shown strong associations to clinical and subclinical CeVD. Other new retinal vascular imaging techniques (dynamic retinal vessel analysis, adaptive optics, and optical coherence tomography angiography) are emerging technologies in this field. Application of AI-DL is expected to detect subclinical retinal changes and discrete retinal features in predicting systemic conditions including CeVD. CONCLUSIONS There is extensive and increasing evidence that a range of retinal vascular signs and disease are closely linked to CeVD, including subclinical and clinical CeVD. New technology including AI-DL will allow further translation to clinical utilization.
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Rim TH, Lee AY, Ting DS, Teo K, Betzler BK, Teo ZL, Yoo TK, Lee G, Kim Y, Lin AC, Kim SE, Tham YC, Kim SS, Cheng CY, Wong TY, Cheung CMG. Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm. Br J Ophthalmol 2020; 105:1133-1139. [PMID: 32907811 DOI: 10.1136/bjophthalmol-2020-316984] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 07/04/2020] [Accepted: 07/28/2020] [Indexed: 12/31/2022]
Abstract
BACKGROUND The ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans has been previously established. We herewith evaluate the ability of a DL model, showing excellent performance on a Korean data set, to generalse onto an American data set despite ethnic differences. In addition, expert graders were surveyed to verify if the DL model was appropriately identifying lesions indicative of nAMD on the OCT scans. METHODS Model development data set-12 247 OCT scans from South Korea; external validation data set-91 509 OCT scans from Washington, USA. In both data sets, normal eyes or eyes with nAMD were included. After internal testing, the algorithm was sent to the University of Washington, USA, for external validation. Area under the receiver operating characteristic curve (AUC) and precision-recall curve (AUPRC) were calculated. For model explanation, saliency maps were generated using Guided GradCAM. RESULTS On external validation, AUC and AUPRC remained high at 0.952 (95% CI 0.942 to 0.962) and 0.891 (95% CI 0.875 to 0.908) at the individual level. Saliency maps showed that in normal OCT scans, the fovea was the main area of interest; in nAMD OCT scans, the appropriate pathological features were areas of model interest. Survey of 10 retina specialists confirmed this. CONCLUSION Our DL algorithm exhibited high performance for nAMD identification in a Korean population, and generalised well to an ethnically distinct, American population. The model correctly focused on the differences within the macular area to extract features associated with nAMD.
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Affiliation(s)
- Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington, USA
| | - Daniel S Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Kelvin Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | | | - Zhen Ling Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Tea Keun Yoo
- Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, Cheongju, Korea (the Republic of)
| | | | | | - Andrew C Lin
- Department of Ophthalmology, NYU Langone Health, New York University School of Medicine, New York, New York, USA
| | - Seong Eun Kim
- Department of Opthalmology, CHA Bundang Medical Center, CHA Univerisity, Seongnam, South Korea
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Sung Soo Kim
- Department of Opthalmology, Yonsei University College of Medicine, Severance Hospital, Institute of Vision Research, Seoul, South Korea
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
| | - Chui Ming Gemmy Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
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9
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Alqudah AM. AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images. Med Biol Eng Comput 2019; 58:41-53. [DOI: 10.1007/s11517-019-02066-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2019] [Accepted: 11/02/2019] [Indexed: 01/16/2023]
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Tan Z, Simkin S, Lai C, Dai S. Deep Learning Algorithm for Automated Diagnosis of Retinopathy of Prematurity Plus Disease. Transl Vis Sci Technol 2019; 8:23. [PMID: 31819832 PMCID: PMC6892443 DOI: 10.1167/tvst.8.6.23] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Accepted: 09/30/2019] [Indexed: 12/20/2022] Open
Abstract
PURPOSE This study describes the initial development of a deep learning algorithm, ROP.AI, to automatically diagnose retinopathy of prematurity (ROP) plus disease in fundal images. METHODS ROP.AI was trained using 6974 fundal images from Australasian image databases. Each image was given a diagnosis as part of real-world routine ROP screening and classified as normal or plus disease. The algorithm was trained using 80% of the images and validated against the remaining 20% within a hold-out test set. Performance in diagnosing plus disease was evaluated against an external set of 90 images. Performance in detecting pre-plus disease was also tested. As a screening tool, the algorithm's operating point was optimized for sensitivity and negative predictive value, and its performance reevaluated. RESULTS For plus disease diagnosis within the 20% hold-out test set, the algorithm achieved a 96.6% sensitivity, 98.0% specificity, and 97.3% ± 0.7% accuracy. Area under the receiver operating characteristic curve was 0.993. Within the independent test set, the algorithm achieved a 93.9% sensitivity, 80.7% specificity, and 95.8% negative predictive value. For detection of pre-plus and plus disease, the algorithm achieved 81.4% sensitivity, 80.7% specificity, and 80.7% negative predictive value. Following the identification of an optimized operating point, the algorithm diagnosed plus disease with a 97.0% sensitivity and 97.8% negative predictive value. CONCLUSIONS ROP.AI is a deep learning algorithm able to automatically diagnose ROP plus disease with high sensitivity and negative predictive value. TRANSLATIONAL RELEVANCE In the context of increasing global disease burden, future development may improve access to ROP diagnosis and care.
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Affiliation(s)
- Zachary Tan
- Save Sight Institute, The University of Sydney, Sydney, New South Wales, Australia
- St Vincent's Hospital Sydney, Sydney, New South Wales, Australia
- Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Samantha Simkin
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Connie Lai
- Queen Mary Hospital, Hong Kong, China
- Department of Ophthalmology, The University of Hong Kong, Hong Kong, China
| | - Shuan Dai
- Department of Ophthalmology, New Zealand National Eye Centre, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Department of Ophthalmology, Queensland Children's Hospital, Brisbane, Queensland, Australia
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Gupta K, Campbell JP, Taylor S, Brown JM, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kalpathy-Cramer J, Kim SJ, Chiang MF. A Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning to Monitor Disease Regression After Treatment. JAMA Ophthalmol 2019; 137:1029-1036. [PMID: 31268499 PMCID: PMC6613298 DOI: 10.1001/jamaophthalmol.2019.2442] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/14/2019] [Indexed: 01/10/2023]
Abstract
Importance Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but treatment failure and disease recurrence are important causes of adverse outcomes in patients with treatment-requiring ROP (TR-ROP). Objectives To apply an automated ROP vascular severity score obtained using a deep learning algorithm and to assess its utility for objectively monitoring ROP regression after treatment. Design, Setting, and Participants This retrospective cohort study used data from the Imaging and Informatics in ROP consortium, which comprises 9 tertiary referral centers in North America that screen high volumes of at-risk infants for ROP. Images of 5255 clinical eye examinations from 871 infants performed between July 2011 and December 2016 were assessed for eligibility in the present study. The disease course was assessed with time across the numerous examinations for patients with TR-ROP. Infants born prematurely meeting screening criteria for ROP who developed TR-ROP and who had images captured within 4 weeks before and after treatment as well as at the time of treatment were included. Main Outcomes and Measures The primary outcome was mean (SD) ROP vascular severity score before, at time of, and after treatment. A deep learning classifier was used to assign a continuous ROP vascular severity score, which ranged from 1 (normal) to 9 (most severe), at each examination. A secondary outcome was the difference in ROP vascular severity score among eyes treated with laser or the vascular endothelial growth factor antagonist bevacizumab. Differences between groups for both outcomes were assessed using unpaired 2-tailed t tests with Bonferroni correction. Results Of 5255 examined eyes, 91 developed TR-ROP, of which 46 eyes met the inclusion criteria based on the available images. The mean (SD) birth weight of those patients was 653 (185) g, with a mean (SD) gestational age of 24.9 (1.3) weeks. The mean (SD) ROP vascular severity scores significantly increased 2 weeks prior to treatment (4.19 [1.75]), peaked at treatment (7.43 [1.89]), and decreased for at least 2 weeks after treatment (4.00 [1.88]) (all P < .001). Eyes requiring retreatment with laser had higher ROP vascular severity scores at the time of initial treatment compared with eyes receiving a single treatment (P < .001). Conclusions and Relevance This quantitative ROP vascular severity score appears to consistently reflect clinical disease progression and posttreatment regression in eyes with TR-ROP. These study results may have implications for the monitoring of patients with ROP for treatment failure and disease recurrence and for determining the appropriate level of disease severity for primary treatment in eyes with aggressive disease.
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Affiliation(s)
- Kishan Gupta
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Stanford Taylor
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - James M. Brown
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago
| | - Jennifer Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Stratis Ioannidis
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
- Massachusetts General Hospital & Brigham and Women’s Hospital Center for Clinical Data Science, Boston
| | - Sang J. Kim
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Michael F. Chiang
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland
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Taylor S, Brown JM, Gupta K, Campbell JP, Ostmo S, Chan RVP, Dy J, Erdogmus D, Ioannidis S, Kim SJ, Kalpathy-Cramer J, Chiang MF. Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning. JAMA Ophthalmol 2019; 137:1022-1028. [PMID: 31268518 PMCID: PMC6613341 DOI: 10.1001/jamaophthalmol.2019.2433] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 04/14/2019] [Indexed: 01/08/2023]
Abstract
Importance Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide, but clinical diagnosis is subjective and qualitative. Objective To describe a quantitative ROP severity score derived using a deep learning algorithm designed to evaluate plus disease and to assess its utility for objectively monitoring ROP progression. Design, Setting, and Participants This retrospective cohort study included images from 5255 clinical examinations of 871 premature infants who met the ROP screening criteria of the Imaging and Informatics in ROP (i-ROP) Consortium, which comprises 9 tertiary care centers in North America, from July 1, 2011, to December 31, 2016. Data analysis was performed from July 2017 to May 2018. Exposure A deep learning algorithm was used to assign a continuous ROP vascular severity score from 1 (most normal) to 9 (most severe) at each examination based on a single posterior photograph compared with a reference standard diagnosis (RSD) simplified into 4 categories: no ROP, mild ROP, type 2 ROP or pre-plus disease, or type 1 ROP. Disease course was assessed longitudinally across multiple examinations for all patients. Main Outcomes and Measures Mean ROP vascular severity score progression over time compared with the RSD. Results A total of 5255 clinical examinations from 871 infants (mean [SD] gestational age, 27.0 [2.0] weeks; 493 [56.6%] male; mean [SD] birth weight, 949 [271] g) were analyzed. The median severity scores for each category were as follows: 1.1 (interquartile range [IQR], 1.0-1.5) (no ROP), 1.5 (IQR, 1.1-3.4) (mild ROP), 4.6 (IQR, 2.4-5.3) (type 2 and pre-plus), and 7.5 (IQR, 5.0-8.7) (treatment-requiring ROP) (P < .001). When the long-term differences in the median severity scores across time between the eyes progressing to treatment and those who did not eventually require treatment were compared, the median score was higher in the treatment group by 0.06 at 30 to 32 weeks, 0.75 at 32 to 34 weeks, 3.56 at 34 to 36 weeks, 3.71 at 36 to 38 weeks, and 3.24 at 38 to 40 weeks postmenstrual age (P < .001 for all comparisons). Conclusions and Relevance The findings suggest that the proposed ROP vascular severity score is associated with category of disease at a given point in time and clinical progression of ROP in premature infants. Automated image analysis may be used to quantify clinical disease progression and identify infants at high risk for eventually developing treatment-requiring ROP. This finding has implications for quality and delivery of ROP care and for future approaches to disease classification.
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Affiliation(s)
- Stanford Taylor
- Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
| | - James M. Brown
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - Kishan Gupta
- Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - Jennifer Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Stratis Ioannidis
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Sang J. Kim
- Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
- Massachusetts General Hospital and Brigham and Women’s Hospital Center for Clinical Data Science, Boston
| | - Michael F. Chiang
- Department of Ophthalmology, Casey Eye institute, Oregon Health & Science University, Portland
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland
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Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy. Symmetry (Basel) 2019. [DOI: 10.3390/sym11060749] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.
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Ting DSW, Cheung CY, Nguyen Q, Sabanayagam C, Lim G, Lim ZW, Tan GSW, Soh YQ, Schmetterer L, Wang YX, Jonas JB, Varma R, Lee ML, Hsu W, Lamoureux E, Cheng CY, Wong TY. Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study. NPJ Digit Med 2019; 2:24. [PMID: 31304371 PMCID: PMC6550209 DOI: 10.1038/s41746-019-0097-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2019] [Accepted: 03/01/2019] [Indexed: 02/07/2023] Open
Abstract
In any community, the key to understanding the burden of a specific condition is to conduct an epidemiological study. The deep learning system (DLS) recently showed promising diagnostic performance for diabetic retinopathy (DR). This study aims to use DLS as the grading tool, instead of human assessors, to determine the prevalence and the systemic cardiovascular risk factors for DR on fundus photographs, in patients with diabetes. This is a multi-ethnic (5 races), multi-site (8 datasets from Singapore, USA, Hong Kong, China and Australia), cross-sectional study involving 18,912 patients (n = 93,293 images). We compared these results and the time taken for DR assessment by DLS versus 17 human assessors - 10 retinal specialists/ophthalmologists and 7 professional graders). The estimation of DR prevalence between DLS and human assessors is comparable for any DR, referable DR and vision-threatening DR (VTDR) (Human assessors: 15.9, 6.5% and 4.1%; DLS: 16.1%, 6.4%, 3.7%). Both assessment methods identified similar risk factors (with comparable AUCs), including younger age, longer diabetes duration, increased HbA1c and systolic blood pressure, for any DR, referable DR and VTDR (p > 0.05). The total time taken for DLS to evaluate DR from 93,293 fundus photographs was ~1 month compared to 2 years for human assessors. In conclusion, the prevalence and systemic risk factors for DR in multi-ethnic population could be determined accurately using a DLS, in significantly less time than human assessors. This study highlights the potential use of AI for future epidemiology or clinical trials for DR grading in the global communities.
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Grants
- This project received funding from National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore (National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022); Health Service Research Grant; SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation. For Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008 & STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550, 09/1/35/19/616). The Singapore Diabetic Retinopathy Program (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 & AIC/HPD/FY2016/0912)
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Affiliation(s)
- Daniel S. W. Ting
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Quang Nguyen
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
| | - Charumathi Sabanayagam
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Gilbert Lim
- National University of Singapore, School of Computing, Singapore, Singapore
| | - Zhan Wei Lim
- National University of Singapore, School of Computing, Singapore, Singapore
| | - Gavin S. W. Tan
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
| | - Yu Qiang Soh
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
| | - Leopold Schmetterer
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Department of Ophthalmology, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria
- Centre for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Ya Xing Wang
- Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Jost B. Jonas
- Beijing Key Laboratory of Ophthalmology and Visual Sciences, Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
- Department of Ophthalmology, Medical Faculty Mannheim of the Ruprecht-Karls-University, Mannheim, Germany
| | - Rohit Varma
- University of Southern California Gayle and Edward Roski Eye Institute, Los Angeles, CA USA
| | - Mong Li Lee
- National University of Singapore, School of Computing, Singapore, Singapore
| | - Wynne Hsu
- National University of Singapore, School of Computing, Singapore, Singapore
| | - Ecosse Lamoureux
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore
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