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Domalpally A, Slater R, Linderman RE, Balaji R, Bogost J, Voland R, Pak J, Blodi BA, Channa R, Fong D, Chew EY. Strong versus Weak Data Labeling for Artificial Intelligence Algorithms in the Measurement of Geographic Atrophy. OPHTHALMOLOGY SCIENCE 2024; 4:100477. [PMID: 38827491 PMCID: PMC11141255 DOI: 10.1016/j.xops.2024.100477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/15/2023] [Accepted: 01/19/2024] [Indexed: 06/04/2024]
Abstract
Purpose To gain an understanding of data labeling requirements to train deep learning models for measurement of geographic atrophy (GA) with fundus autofluorescence (FAF) images. Design Evaluation of artificial intelligence (AI) algorithms. Subjects The Age-Related Eye Disease Study 2 (AREDS2) images were used for training and cross-validation, and GA clinical trial images were used for testing. Methods Training data consisted of 2 sets of FAF images; 1 with area measurements only and no indication of GA location (Weakly labeled) and the second with GA segmentation masks (Strongly labeled). Main Outcome Measures Bland-Altman plots and scatter plots were used to compare GA area measurement between ground truth and AI measurements. The Dice coefficient was used to compare accuracy of segmentation of the Strong model. Results In the cross-validation AREDS2 data set (n = 601), the mean (standard deviation [SD]) area of GA measured by human grader, Weakly labeled AI model, and Strongly labeled AI model was 6.65 (6.3) mm2, 6.83 (6.29) mm2, and 6.58 (6.24) mm2, respectively. The mean difference between ground truth and AI was 0.18 mm2 (95% confidence interval, [CI], -7.57 to 7.92) for the Weakly labeled model and -0.07 mm2 (95% CI, -1.61 to 1.47) for the Strongly labeled model. With GlaxoSmithKline testing data (n = 156), the mean (SD) GA area was 9.79 (5.6) mm2, 8.82 (4.61) mm2, and 9.55 (5.66) mm2 for human grader, Strongly labeled AI model, and Weakly labeled AI model, respectively. The mean difference between ground truth and AI for the 2 models was -0.97 mm2 (95% CI, -4.36 to 2.41) and -0.24 mm2 (95% CI, -4.98 to 4.49), respectively. The Dice coefficient was 0.99 for intergrader agreement, 0.89 for the cross-validation data, and 0.92 for the testing data. Conclusions Deep learning models can achieve reasonable accuracy even with Weakly labeled data. Training methods that integrate large volumes of Weakly labeled images with small number of Strongly labeled images offer a promising solution to overcome the burden of cost and time for data labeling. 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)
- Amitha Domalpally
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Robert Slater
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Rachel E. Linderman
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Rohit Balaji
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Jacob Bogost
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Rick Voland
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Jeong Pak
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Barbara A. Blodi
- A-EYE Research Unit, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | - Roomasa Channa
- Wisconsin Reading Center, Department of Ophthalmology and Visual Sciences, University of Wisconsin, Madison, Wisconsin
| | | | - Emily Y. Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
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Schmidt-Erfurth U, Mai J, Reiter GS, Riedl S, Vogl WD, Sadeghipour A, McKeown A, Foos E, Scheibler L, Bogunovic H. Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT. Ophthalmology 2024:S0161-6420(24)00487-1. [PMID: 39151755 DOI: 10.1016/j.ophtha.2024.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 07/29/2024] [Accepted: 08/08/2024] [Indexed: 08/19/2024] Open
Abstract
PURPOSE To quantify morphological changes of the photoreceptors (PR) and retinal pigment epithelium (RPE) layers under pegcetacoplan therapy in geographic atrophy (GA) using deep learning-based analysis of optical coherence tomography (OCT) images. DESIGN Post-hoc longitudinal image analysis SUBJECTS: Patients with GA due to age-related macular degeneration from two prospective randomized phase III clinical trials (OAKS and DERBY) METHODS: Deep learning-based segmentation of RPE loss and PR degeneration, defined as loss of the ellipsoid zone (EZ) layer on OCT, over 24 months on SD-OCT images MAIN OUTCOME MEASURES: Change in the mean area of RPE loss and EZ loss over time in the pooled sham arms and the monthly (PM)/every other month (PEOM) treatment arms RESULTS: 897 eyes of 897 patients were included. There was a therapeutic reduction of RPE loss growth by 22%/20% in OAKS and 27%/21% in DERBY for PM/PEOM compared to sham, respectively, at 24 months. The reduction on the EZ level was significantly higher with 53%/46% in OAKS and 47%/46% in DERBY for PM/PEOM compared to sham at 24 months. The baseline EZ-RPE difference had an impact on disease activity and therapeutic response. The therapeutic benefit for RPE loss growth increased with larger EZ-RPE difference quartiles from 21.9%, 23.1%, 23.9% to 33.6% for PM vs. sham (all p<0.01) and from 13.6% (p=0.11), 23.8%, 23.8% to 20.0% for PEOM vs. sham (p<0.01) in quartiles 1,2,3 and 4, respectively, at 24 months. Regarding EZ layer maintenance, the therapeutic reduction of loss increased from 14.8% (p=0.09), 33.3%, 46.6% to 77.8% (p<0.0001) between PM and sham and from 15.9% (p=0.08), 33.8%, 52.0% to 64.9% (p<0.0001) between PEOM and sham for quartiles 1-4 at 24 months. CONCLUSION OCT-based AI analysis objectively identifies and quantifies PR and RPE degeneration in GA. Reductions in further PR degeneration consistent with EZ loss on OCT are even higher than the effect on RPE loss in phase 3 trials of pegcetacoplan treatment. The EZ-RPE difference has a strong impact on disease progression and therapeutic response. Identification of patients with higher EZ-RPE loss difference may become an important criterion for the management of GA secondary to AMD.
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Affiliation(s)
- Ursula Schmidt-Erfurth
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.
| | - Julia Mai
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Gregor S Reiter
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Sophie Riedl
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | | | | | - Alex McKeown
- Apellis Pharmaceuticals, Boston, Massachusetts, United States
| | - Emma Foos
- Apellis Pharmaceuticals, Boston, Massachusetts, United States
| | - Lukas Scheibler
- Apellis Pharmaceuticals, Boston, Massachusetts, United States
| | - Hrvoje Bogunovic
- OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
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Nakayama LF, Restrepo D, Matos J, Ribeiro LZ, Malerbi FK, Celi LA, Regatieri CS. BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos. PLOS DIGITAL HEALTH 2024; 3:e0000454. [PMID: 38991014 PMCID: PMC11239107 DOI: 10.1371/journal.pdig.0000454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 06/03/2024] [Indexed: 07/13/2024]
Abstract
INTRODUCTION The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. METHODS Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used a ConvNext model trained during 50 epochs using a weighted cross entropy loss to avoid overfitting, with 70% training (20% validation), and 30% testing subsets. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Saliency maps were calculated for interpretability. RESULTS BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 (± 18.26) years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. A ConvNext V2 model was trained and evaluated BRSET in four prediction tasks: "binary diabetic retinopathy diagnosis (Normal vs Diabetic Retinopathy)" (AUC: 97, F1: 89); "3 class diabetic retinopathy diagnosis (Normal, Proliferative, Non-Proliferative)" (AUC: 97, F1: 82); "diabetes diagnosis" (AUC: 91, F1: 83); "sex classification" (AUC: 87, F1: 70). DISCUSSION BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.
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Affiliation(s)
- Luis Filipe Nakayama
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - David Restrepo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Telematics Department, University of Cauca, Popayán, Cauca, Colombia
| | - João Matos
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Faculty of Engineering of University of Porto, Porto, Portugal
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
| | - Fernando Korn Malerbi
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America
| | - Caio Saito Regatieri
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
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Papaioannou C. Advancements in the treatment of age-related macular degeneration: a comprehensive review. Postgrad Med J 2024; 100:445-450. [PMID: 38330506 DOI: 10.1093/postmj/qgae016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/04/2024] [Accepted: 01/16/2024] [Indexed: 02/10/2024]
Abstract
Age-related macular degeneration (AMD) stands as a leading cause of irreversible blindness, particularly affecting central vision and impeding daily tasks. This paper provides a thorough exploration of AMD, distinguishing between its two main subtypes-Wet and Dry AMD-while shedding light on the prevalence and risk factors, including age, genetics, and smoking. The focus shifts to the current and future treatment landscape, examining both Dry and Wet AMD. Regarding Dry AMD, interventions such as antioxidant supplementation and ongoing clinical trials offer hope. Notable among these is Pegcetacoplan which is the only Food and Drug Administration (FDA)-approved medication, displaying promising results in reducing geographic atrophy lesions. For Wet AMD, anti-Vascular Endothelial Growth Factor therapies like Ranibizumab (Lucentis®) have been instrumental, and newer drugs like Faricimab and OPT-302 show comparable efficacy with extended dosing intervals. Additionally, gene therapies such as RGX-314 present a potential paradigm shift, reducing or eliminating the need for frequent injections. Biosimilars offer cost-effective alternatives. The paper also delves into the integration of technology and artificial intelligence in AMD management, highlighting the role of smartphone apps for patient monitoring and artificial intelligence algorithms for diagnosis and surveillance. Furthermore, patient perspectives on artificial intelligence demonstrate a positive correlation between understanding and trust. The narrative concludes with a glimpse into ground-breaking technologies, including retinal implants and bionic chips, offering hope for vision restoration. Overall, this paper underscores the multifaceted approach in addressing AMD, combining traditional and innovative strategies, paving the way for a more promising future in AMD treatment.
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Affiliation(s)
- Christos Papaioannou
- Department of Surgery, Chelsea and Westminster Hospital NHS Foundation Trust, TW7 6AF, London, United Kingdom
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Fu DJ, Bagga P, Naik G, Glinton S, Faes L, Liefers B, Lima R, Wignall G, Keane PA, Ioannidou E, Ribeiro Reis AP, McKeown A, Scheibler L, Patel PJ, Moghul I, Pontikos N, Balaskas K. Pegcetacoplan Treatment and Consensus Features of Geographic Atrophy Over 24 Months. JAMA Ophthalmol 2024; 142:548-558. [PMID: 38722644 PMCID: PMC11082756 DOI: 10.1001/jamaophthalmol.2024.1269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 02/24/2024] [Indexed: 05/12/2024]
Abstract
Importance Despite widespread availability and consensus on its advantages for detailed imaging of geographic atrophy (GA), spectral-domain optical coherence tomography (SD-OCT) might benefit from automated quantitative OCT analyses in GA diagnosis, monitoring, and reporting of its landmark clinical trials. Objective To analyze the association between pegcetacoplan and consensus GA SD-OCT end points. Design, Setting, and Participants This was a post hoc analysis of 11 614 SD-OCT volumes from 936 of the 1258 participants in 2 parallel phase 3 studies, the Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (OAKS) and Study to Compare the Efficacy and Safety of Intravitreal APL-2 Therapy With Sham Injections in Patients With Geographic Atrophy (GA) Secondary to Age-Related Macular Degeneration (DERBY). OAKS and DERBY were 24-month, multicenter, randomized, double-masked, sham-controlled studies conducted from August 2018 to July 2020 among adults with GA with total area 2.5 to 17.5 mm2 on fundus autofluorescence imaging (if multifocal, at least 1 lesion ≥1.25 mm2). This analysis was conducted from September to December 2023. Interventions Study participants received pegcetacoplan, 15 mg per 0.1-mL intravitreal injection, monthly or every other month, or sham injection monthly or every other month. Main Outcomes and Measures The primary end point was the least squares mean change from baseline in area of retinal pigment epithelium and outer retinal atrophy in each of the 3 treatment arms (pegcetacoplan monthly, pegcetacoplan every other month, and pooled sham [sham monthly and sham every other month]) at 24 months. Feature-specific area analysis was conducted by Early Treatment Diabetic Retinopathy Study (ETDRS) regions of interest (ie, foveal, parafoveal, and perifoveal). Results Among 936 participants, the mean (SD) age was 78.5 (7.22) years, and 570 participants (60.9%) were female. Pegcetacoplan, but not sham treatment, was associated with reduced growth rates of SD-OCT biomarkers for GA for up to 24 months. Reductions vs sham in least squares mean (SE) change from baseline of retinal pigment epithelium and outer retinal atrophy area were detectable at every time point from 3 through 24 months (least squares mean difference vs pooled sham at month 24, pegcetacoplan monthly: -0.86 mm2; 95% CI, -1.15 to -0.57; P < .001; pegcetacoplan every other month: -0.69 mm2; 95% CI, -0.98 to -0.39; P < .001). This association was more pronounced with more frequent dosing (pegcetacoplan monthly vs pegcetacoplan every other month at month 24: -0.17 mm2; 95% CI, -0.43 to 0.08; P = .17). Stronger associations were observed in the parafoveal and perifoveal regions for both pegcetacoplan monthly and pegcetacoplan every other month. Conclusions and Relevance These findings offer additional insight into the potential effects of pegcetacoplan on the development of GA, including potential effects on the retinal pigment epithelium and photoreceptors. Trial Registration ClinicalTrials.gov Identifiers: NCT03525600 and NCT03525613.
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Affiliation(s)
- Dun Jack Fu
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Pallavi Bagga
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Gunjan Naik
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Sophie Glinton
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Livia Faes
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Bart Liefers
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Rosana Lima
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Georgina Wignall
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Pearse A. Keane
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Estelle Ioannidou
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Ana Paula Ribeiro Reis
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | | | | | - Praveen J. Patel
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Ismail Moghul
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Nikolas Pontikos
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
| | - Konstantinos Balaskas
- National Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital and University College London Institute of Ophthalmology, London, United Kingdom
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Yao H, Wu Z, Gao SS, Guymer RH, Steffen V, Chen H, Hejrati M, Zhang M. Deep Learning Approaches for Detecting of Nascent Geographic Atrophy in Age-Related Macular Degeneration. OPHTHALMOLOGY SCIENCE 2024; 4:100428. [PMID: 38284101 PMCID: PMC10818248 DOI: 10.1016/j.xops.2023.100428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 10/31/2023] [Accepted: 11/08/2023] [Indexed: 01/30/2024]
Abstract
Purpose Nascent geographic atrophy (nGA) refers to specific features seen on OCT B-scans, which are strongly associated with the future development of geographic atrophy (GA). This study sought to develop a deep learning model to screen OCT B-scans for nGA that warrant further manual review (an artificial intelligence [AI]-assisted approach), and to determine the extent of reduction in OCT B-scan load requiring manual review while maintaining near-perfect nGA detection performance. Design Development and evaluation of a deep learning model. Participants One thousand eight hundred and eighty four OCT volume scans (49 B-scans per volume) without neovascular age-related macular degeneration from 280 eyes of 140 participants with bilateral large drusen at baseline, seen at 6-monthly intervals up to a 36-month period (from which 40 eyes developed nGA). Methods OCT volume and B-scans were labeled for the presence of nGA. Their presence at the volume scan level provided the ground truth for training a deep learning model to identify OCT B-scans that potentially showed nGA requiring manual review. Using a threshold that provided a sensitivity of 0.99, the B-scans identified were assigned the ground truth label with the AI-assisted approach. The performance of this approach for detecting nGA across all visits, or at the visit of nGA onset, was evaluated using fivefold cross-validation. Main Outcome Measures Sensitivity for detecting nGA, and proportion of OCT B-scans requiring manual review. Results The AI-assisted approach (utilizing outputs from the deep learning model to guide manual review) had a sensitivity of 0.97 (95% confidence interval [CI] = 0.93-1.00) and 0.95 (95% CI = 0.87-1.00) for detecting nGA across all visits and at the visit of nGA onset, respectively, when requiring manual review of only 2.7% and 1.9% of selected OCT B-scans, respectively. Conclusions A deep learning model could be used to enable near-perfect detection of nGA onset while reducing the number of OCT B-scans requiring manual review by over 50-fold. This AI-assisted approach shows promise for substantially reducing the current burden of manual review of OCT B-scans to detect this crucial feature that portends future development of GA. 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)
- Heming Yao
- gRED Computational Science, Genentech, Inc., South San Francisco, California
| | - Zhichao Wu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
- Ophthalmology Division, Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Simon S. Gao
- gRED Computational Science, Genentech, Inc., South San Francisco, California
| | - Robyn H. Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia
- Ophthalmology Division, Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Verena Steffen
- gRED Computational Science, Genentech, Inc., South San Francisco, California
| | - Hao Chen
- gRED Computational Science, Genentech, Inc., South San Francisco, California
| | - Mohsen Hejrati
- gRED Computational Science, Genentech, Inc., South San Francisco, California
| | - Miao Zhang
- gRED Computational Science, Genentech, Inc., South San Francisco, California
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Mahmoudi A, Corradetti G, Emamverdi M, Lindenberg S, He Y, Oncel D, Santina A, Baek J, Kadomoto S, Nittala MG, Sadda SR. Atrophic Lesions Associated with Age-Related Macular Degeneration: High-Resolution versus Standard OCT. Ophthalmol Retina 2024; 8:367-375. [PMID: 37871680 DOI: 10.1016/j.oret.2023.10.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 09/12/2023] [Accepted: 10/16/2023] [Indexed: 10/25/2023]
Abstract
OBJECTIVE The objective of this study was to determine whether high-resolution OCT (HR-OCT) could enhance the identification and classification of atrophic features in age-related macular degeneration (AMD) compared with standard resolution OCT. DESIGN Prospective, observational, cross-sectional study. SUBJECTS The study included 60 eyes from 60 patients > 60 years of age with a diagnosis of AMD. METHODS The participants underwent volume OCT scanning using HR-OCT and standard resolution OCT devices. Trained graders reviewed and graded the scans, identifying specific regions of interest for subsequent analysis. MAIN OUTCOME MEASURES The study focused on identifying and classifying complete retinal pigment epithelium (RPE) and outer retinal atrophy (cRORA), incomplete RORA (iRORA), and other nonatrophic AMD features. Additionally, qualitative and quantitative features associated with atrophy were assessed. RESULTS The agreement among readers for classifying atrophic lesions was substantial to perfect for both HR-OCT (0.88) and standard resolution OCT(0.82). However, HR-OCT showed a higher accuracy in identifying iRORA lesions compared with standard OCT. Qualitative assessment of features demonstrated higher agreement for HR-OCT, particularly in identifying external limiting membrane (ELM) (0.95) and ellipsoid zone (EZ) disruption (0.94). Quantitative measurements of features such as hypertransmission defects, RPE attenuation/disruption, EZ disruption width, and ELM disruption width showed excellent interreader agreement with HR-OCT (> 0.90 for all features) but only moderate agreement with standard OCT (0.51-0.60). CONCLUSIONS The study results suggest that HR-OCT improves the accuracy and reliability of classifying and quantifying atrophic lesions associated with AMD compared with standard resolution OCT. The quantitative findings in particular may have implications for future research and clinical practice, especially with the availability of therapeutic agents for treating geographic atrophy and the development of commercially available HR-OCT devices. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Alireza Mahmoudi
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
| | - Giulia Corradetti
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Mehdi Emamverdi
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Sophiana Lindenberg
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ye He
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California; Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Deniz Oncel
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Ahmad Santina
- Stein Eye Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Jiwon Baek
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, Bucheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Bucheon, Republic of Korea
| | - Shin Kadomoto
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - Muneeswar Gupta Nittala
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California
| | - SriniVas R Sadda
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California.
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Woof W, de Guimarães TAC, Al-Khuzaei S, Varela MD, Sen S, Bagga P, Mendes B, Shah M, Burke P, Parry D, Lin S, Naik G, Ghoshal B, Liefers B, Fu DJ, Georgiou M, Nguyen Q, da Silva AS, Liu Y, Fujinami-Yokokawa Y, Kabiri N, Sumodhee D, Patel P, Furman J, Moghul I, Sallum J, De Silva SR, Lorenz B, Holz F, Fujinami K, Webster AR, Mahroo O, Downes SM, Madhusuhan S, Balaskas K, Michaelides M, Pontikos N. Quantification of Fundus Autofluorescence Features in a Molecularly Characterized Cohort of More Than 3000 Inherited Retinal Disease Patients from the United Kingdom. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.24.24304809. [PMID: 38585957 PMCID: PMC10996753 DOI: 10.1101/2024.03.24.24304809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Purpose To quantify relevant fundus autofluorescence (FAF) image features cross-sectionally and longitudinally in a large cohort of inherited retinal diseases (IRDs) patients. Design Retrospective study of imaging data (55-degree blue-FAF on Heidelberg Spectralis) from patients. Participants Patients with a clinical and molecularly confirmed diagnosis of IRD who have undergone FAF 55-degree imaging at Moorfields Eye Hospital (MEH) and the Royal Liverpool Hospital (RLH) between 2004 and 2019. Methods Five FAF features of interest were defined: vessels, optic disc, perimacular ring of increased signal (ring), relative hypo-autofluorescence (hypo-AF) and hyper-autofluorescence (hyper-AF). Features were manually annotated by six graders in a subset of patients based on a defined grading protocol to produce segmentation masks to train an AI model, AIRDetect, which was then applied to the entire imaging dataset. Main Outcome Measures Quantitative FAF imaging features including area in mm 2 and vessel metrics, were analysed cross-sectionally by gene and age, and longitudinally to determine rate of progression. AIRDetect feature segmentation and detection were validated with Dice score and precision/recall, respectively. Results A total of 45,749 FAF images from 3,606 IRD patients from MEH covering 170 genes were automatically segmented using AIRDetect. Model-grader Dice scores for disc, hypo-AF, hyper-AF, ring and vessels were respectively 0.86, 0.72, 0.69, 0.68 and 0.65. The five genes with the largest hypo-AF areas were CHM , ABCC6 , ABCA4 , RDH12 , and RPE65 , with mean per-patient areas of 41.5, 30.0, 21.9, 21.4, and 15.1 mm 2 . The five genes with the largest hyper-AF areas were BEST1 , CDH23 , RDH12 , MYO7A , and NR2E3 , with mean areas of 0.49, 0.45, 0.44, 0.39, and 0.34 mm 2 respectively. The five genes with largest ring areas were CDH23 , NR2E3 , CRX , EYS and MYO7A, with mean areas of 3.63, 3.32, 2.84, 2.39, and 2.16 mm 2 . Vessel density was found to be highest in EFEMP1 , BEST1 , TIMP3 , RS1 , and PRPH2 (10.6%, 10.3%, 9.8%, 9.7%, 8.9%) and was lower in Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis genes. Longitudinal analysis of decreasing ring area in four RP genes ( RPGR, USH2A, RHO, EYS ) found EYS to be the fastest progressor at -0.18 mm 2 /year. Conclusions We have conducted the first large-scale cross-sectional and longitudinal quantitative analysis of FAF features across a diverse range of IRDs using a novel AI approach.
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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] [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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Jeong H, Park JE, Kim N, Yoon SK, Kim HS. Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study. Eur Radiol 2024; 34:2062-2071. [PMID: 37658885 PMCID: PMC10873231 DOI: 10.1007/s00330-023-10120-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 06/25/2023] [Accepted: 07/01/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVES We aimed to evaluate whether deep learning-based detection and quantification of brain metastasis (BM) may suggest treatment options for patients with BMs. METHODS The deep learning system (DLS) for detection and quantification of BM was developed in 193 patients and applied to 112 patients that were newly detected on black-blood contrast-enhanced T1-weighted imaging. Patients were assigned to one of 3 treatment suggestion groups according to the European Association of Neuro-Oncology (EANO)-European Society for Medical Oncology (ESMO) recommendations using number and volume of the BMs detected by the DLS: short-term imaging follow-up without treatment (group A), surgery or stereotactic radiosurgery (limited BM, group B), or whole-brain radiotherapy or systemic chemotherapy (extensive BM, group C). The concordance between the DLS-based groups and clinical decisions was analyzed with or without consideration of targeted agents. The performance of distinguishing high-risk (B + C) was calculated. RESULTS Among 112 patients (mean age 64.3 years, 63 men), group C had the largest number and volume of BM, followed by group B (4.4 and 851.6 mm3) and A (1.5 and 15.5 mm3). The DLS-based groups were concordant with the actual clinical decisions, with an accuracy of 76.8% (86 of 112). Modified accuracy considering targeted agents was 81.3% (91 of 112). The DLS showed 95% (82/86) sensitivity and 81% (21/26) specificity for distinguishing the high risk. CONCLUSION DLS-based detection and quantification of BM have the potential to be helpful in the determination of treatment options for both low- and high-risk groups of limited and extensive BMs. CLINICAL RELEVANCE STATEMENT For patients with newly diagnosed brain metastasis, deep learning-based detection and quantification may be used in clinical settings where prompt and accurate treatment decisions are required, which can lead to better patient outcomes. KEY POINTS • Deep learning-based brain metastasis detection and quantification showed excellent agreement with ground-truth classifications. • By setting an algorithm to suggest treatment based on the number and volume of brain metastases detected by the deep learning system, the concordance was 81.3%. • When dividing patients into low- and high-risk groups, the sensitivity for detecting the latter was 95%.
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Affiliation(s)
- Hana Jeong
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea
| | - Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea.
| | | | - Shin-Kyo Yoon
- Department of Oncology, Asan Medical Center, Seoul, South Korea
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 43 Olympic-ro 88, Songpa-Gu, 05505, Seoul, Korea
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Song A, Lusk JB, Roh KM, Hsu ST, Valikodath NG, Lad EM, Muir KW, Engelhard MM, Limkakeng AT, Izatt JA, McNabb RP, Kuo AN. RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population. Transl Vis Sci Technol 2024; 13:12. [PMID: 38488431 PMCID: PMC10946693 DOI: 10.1167/tvst.13.3.12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/31/2024] [Indexed: 03/19/2024] Open
Abstract
Purpose To evaluate the diagnostic performance of a robotically aligned optical coherence tomography (RAOCT) system coupled with a deep learning model in detecting referable posterior segment pathology in OCT images of emergency department patients. Methods A deep learning model, RobOCTNet, was trained and internally tested to classify OCT images as referable versus non-referable for ophthalmology consultation. For external testing, emergency department patients with signs or symptoms warranting evaluation of the posterior segment were imaged with RAOCT. RobOCTNet was used to classify the images. Model performance was evaluated against a reference standard based on clinical diagnosis and retina specialist OCT review. Results We included 90,250 OCT images for training and 1489 images for internal testing. RobOCTNet achieved an area under the curve (AUC) of 1.00 (95% confidence interval [CI], 0.99-1.00) for detection of referable posterior segment pathology in the internal test set. For external testing, RAOCT was used to image 72 eyes of 38 emergency department patients. In this set, RobOCTNet had an AUC of 0.91 (95% CI, 0.82-0.97), a sensitivity of 95% (95% CI, 87%-100%), and a specificity of 76% (95% CI, 62%-91%). The model's performance was comparable to two human experts' performance. Conclusions A robotically aligned OCT coupled with a deep learning model demonstrated high diagnostic performance in detecting referable posterior segment pathology in a cohort of emergency department patients. Translational Relevance Robotically aligned OCT coupled with a deep learning model may have the potential to improve emergency department patient triage for ophthalmology referral.
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Affiliation(s)
- Ailin Song
- Duke University School of Medicine, Durham, NC, USA
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Jay B. Lusk
- Duke University School of Medicine, Durham, NC, USA
| | - Kyung-Min Roh
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - S. Tammy Hsu
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | | | - Eleonora M. Lad
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Kelly W. Muir
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Matthew M. Engelhard
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | | | - Joseph A. Izatt
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ryan P. McNabb
- Department of Ophthalmology, Duke University, Durham, NC, USA
| | - Anthony N. Kuo
- Department of Ophthalmology, Duke University, Durham, NC, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
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Nakayama LF, Restrepo D, Matos J, Ribeiro LZ, Malerbi FK, Celi LA, Regatieri CS. BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.23.24301660. [PMID: 38343827 PMCID: PMC10854338 DOI: 10.1101/2024.01.23.24301660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Introduction The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used Dino V2 Base for feature extraction, with 70% training and 30% testing subsets. Support Vector Machines (SVM) and Logistic Regression (LR) were employed with weighted training. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Results BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. Models were trained on BRSET in three prediction tasks: "diabetes diagnosis"; "sex classification"; and "diabetic retinopathy diagnosis". Discussion BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.
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Affiliation(s)
- Luis Filipe Nakayama
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - David Restrepo
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Telematics Department, University of Cauca, Popayán, Cauca, Colombia
| | - João Matos
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Faculty of Engineering of University of Porto, Porto, Portugal
| | - Lucas Zago Ribeiro
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
| | - Fernando Korn Malerbi
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
| | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Caio Saito Regatieri
- Department of Ophthalmology, São Paulo Federal University, São Paulo, São Paulo, Brazil
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Nanegrungsunk O, Corradetti G, Phinyo P, Choovuthayakorn J, Sadda SR. PREVALENCE AND PERSISTENCE OF HYPERTRANSMISSION DEFECTS OF VARIOUS SIZES IN EYES WITH INTERMEDIATE AGE-RELATED MACULAR DEGENERATION. Retina 2024; 44:20-27. [PMID: 37683194 DOI: 10.1097/iae.0000000000003929] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
PURPOSE To determine the prevalence and rate of persistence over 2 years of various-sized hypertransmission defects (hyperTDs) in eyes with intermediate age-related macular degeneration. METHODS Retrospective analysis of optical coherence tomography data from consecutive intermediate age-related macular degeneration patients. Choroidal en face optical coherence tomography images were evaluated for the presence and number of hyperTDs of three different sizes based on greatest linear dimension (small, 63-124 µ m; medium, 125-249 µ m; large, ≥250 µ m) at baseline and at the 2-year follow-up. Interreader agreement was determined by Gwet's agreement coefficient. Disagreements between graders were resolved by the senior investigator to yield a single consensus for all cases. RESULTS From 273 intermediate age-related macular degeneration eyes (247 patients), 72 and 76 hyperTD lesions were independently identified by two graders at baseline and overall agreement coefficient was 0.89 (95% CI, 0.86-0.93). After adjudication by the senior grader, the final consensus yielded 78 hyperTD lesions from 46 eyes (16.8%) of 42 patients (17.0%) in this study cohort. Among eyes with follow-up optical coherence tomography, 32 of 45 hyperTD lesions (71.1%) persisted. The rates of persistence were 100.0%, 72.7%, and 53.3% in large, medium, and small hyperTD sizes, respectively. CONCLUSION HyperTDs were present in a significant proportion of intermediate age-related macular degeneration eyes. Acceptable interreader agreement was demonstrated in identifying hyperTD. Larger hyperTD lesions were more likely to persist over 2 years.
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Affiliation(s)
- Onnisa Nanegrungsunk
- Doheny Imaging Reading Center and Doheny Eye Institute, Pasadena, California
- Department of Ophthalmology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California
- Retina Division, Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; and
| | - Giulia Corradetti
- Doheny Imaging Reading Center and Doheny Eye Institute, Pasadena, California
- Department of Ophthalmology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California
| | - Phichayut Phinyo
- Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand
| | - Janejit Choovuthayakorn
- Retina Division, Department of Ophthalmology, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand; and
| | - Srinivas R Sadda
- Doheny Imaging Reading Center and Doheny Eye Institute, Pasadena, California
- Department of Ophthalmology, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, California
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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] [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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Elsawy A, Keenan TD, Chen Q, Shi X, Thavikulwat AT, Bhandari S, Chew EY, Lu Z. Deep-GA-Net for Accurate and Explainable Detection of Geographic Atrophy on OCT Scans. OPHTHALMOLOGY SCIENCE 2023; 3:100311. [PMID: 37304045 PMCID: PMC10251072 DOI: 10.1016/j.xops.2023.100311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 04/06/2023] [Accepted: 04/07/2023] [Indexed: 06/13/2023]
Abstract
Objective To propose Deep-GA-Net, a 3-dimensional (3D) deep learning network with 3D attention layer, for the detection of geographic atrophy (GA) on spectral domain OCT (SD-OCT) scans, explain its decision making, and compare it with existing methods. Design Deep learning model development. Participants Three hundred eleven participants from the Age-Related Eye Disease Study 2 Ancillary SD-OCT Study. Methods A dataset of 1284 SD-OCT scans from 311 participants was used to develop Deep-GA-Net. Cross-validation was used to evaluate Deep-GA-Net, where each testing set contained no participant from the corresponding training set. En face heatmaps and important regions at the B-scan level were used to visualize the outputs of Deep-GA-Net, and 3 ophthalmologists graded the presence or absence of GA in them to assess the explainability (i.e., understandability and interpretability) of its detections. Main Outcome Measures Accuracy, area under receiver operating characteristic curve (AUC), area under precision-recall curve (APR). Results Compared with other networks, Deep-GA-Net achieved the best metrics, with accuracy of 0.93, AUC of 0.94, and APR of 0.91, and received the best gradings of 0.98 and 0.68 on the en face heatmap and B-scan grading tasks, respectively. Conclusions Deep-GA-Net was able to detect GA accurately from SD-OCT scans. The visualizations of Deep-GA-Net were more explainable, as suggested by 3 ophthalmologists. The code and pretrained models are publicly available at https://github.com/ncbi/Deep-GA-Net. Financial Disclosures The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Amr Elsawy
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - Tiarnan D.L. Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Qingyu Chen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - Xioashuang Shi
- Department of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Alisa T. Thavikulwat
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Sanjeeb Bhandari
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Emily Y. Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Zhiyong Lu
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland
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Schmitz-Valckenberg S, Saßmannshausen M, Braun M, Steffen V, Gao SS, Honigberg L, Ferrara D, Pfau M, Holz FG. Interreader Agreement and Longitudinal Progression of Incomplete/Complete Retinal Pigment Epithelium and Outer Retinal Atrophy in Age-Related Macular Degeneration. Ophthalmol Retina 2023; 7:1059-1068. [PMID: 37517799 DOI: 10.1016/j.oret.2023.07.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE To analyze the ability to evaluate changes over time of individual lesions of incomplete or complete retinal pigment epithelium (RPE) and outer retinal atrophy (iRORA and cRORA, respectively) in patients with intermediate age-related macular degeneration (iAMD). DESIGN OCT images from patients enrolled in Proxima B clinical trial (NCT02399072) were utilized. PARTICIPANTS Patients enrolled in the Proxima B clinical trial, from the cohort with geographic atrophy (GA) in 1 eye and iAMD in the other eye at baseline, were included. METHODS Junior and senior readers analyzed OCT images for the qualitative presence of 9 distinct early atrophic features (presence of zone of choroidal hypertransmission, attenuation and/or disruption of RPE, disruption of ellipsoid zone [EZ] and external limiting membrane [ELM], outer nuclear layer [ONL] thinning, outer plexiform layer [OPL]/inner nuclear layer [INL] subsidence, and hyporeflective wedge-shaped band). If deemed "present," 7 features were quantified with a predefined tolerance level of 50 μm (diameter for the zone of choroidal hypertransmission, zone of attenuation and/or disruption of the RPE, outer retinal thickness left/right vertical diameter, outer retinal thickness thinnest vertical diameter, annotation of EZ, and ELM disruption). MAIN OUTCOME MEASURES Interreader agreements for qualitative assessments (κ-type statistics) and quantitative measurements (Bland-Altman statistics) were assessed. Progression of the lesion features over time was described. RESULTS Moderate agreement was found for presence of choroidal hypertransmission (κ = 0.54), followed by ELM disruption (κ = 0.58), OPL/INL subsidence (κ = 0.46), and a hyporeflective wedge-shaped band (κ = 0.47). Quantification measurements showed that choroidal hypertransmission had the highest agreement, whereas RPE attenuation/disruption had the lowest agreement. Longitudinal adjudicated changes for quantitative measurements of lesion progression showed that choroidal hypertransmission and ELM disruption showed significant progression, whereas EZ disruption and RPE attenuation/disruption did not. CONCLUSIONS The ability to evaluate changes over time for specific features of iRORA and cRORA was explored. The most robust biomarker was found to be choroidal hypertransmission, followed by ELM disruption and the qualitative markers of OPL/INL subsidence, as well as a wedge-shaped band. Disease progression over time could be assessed by some, but not all, spectral-domain OCT features that were explored. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Steffen Schmitz-Valckenberg
- John A. Moran Eye Center, University of Utah, Salt Lake City, Utah; GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany.
| | | | - Martina Braun
- GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany
| | | | - Simon S Gao
- Genentech, Inc., South San Francisco, California
| | | | | | - Maximilian Pfau
- GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Frank G Holz
- GRADE Reading Center and Department of Ophthalmology, University of Bonn, Germany
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Chouraqui M, Crincoli E, Miere A, Meunier IA, Souied EH. Deep learning model for automatic differentiation of EMAP from AMD in macular atrophy. Sci Rep 2023; 13:20354. [PMID: 37990107 PMCID: PMC10663469 DOI: 10.1038/s41598-023-47854-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/19/2023] [Indexed: 11/23/2023] Open
Abstract
To create a deep learning (DL) classifier pre-trained on fundus autofluorescence (FAF) images that can assist the clinician in distinguishing age-related geographic atrophy from extensive macular atrophy and pseudodrusen-like appearance (EMAP). Patients with complete outer retinal and retinal pigment epithelium atrophy secondary to either EMAP (EMAP Group) or to dry age related macular degeneration (AMD group) were retrospectively selected. Fovea-centered posterior pole (30° × 30°) and 55° × 55° degree-field-of-view FAF images of sufficiently high quality were collected and used to train two different deep learning (DL) classifiers based on ResNet-101 design. Testing was performed on a set of images coming from a different center. A total of 300 patients were recruited, 135 belonging to EMAP group and 165 belonging to AMD group. The 30° × 30° FAF based DL classifier showed a sensitivity of 84.6% and a specificity of 85.3% for the diagnosis of EMAP. The 55° × 55° FAF based DL classifier showed a sensitivity of 90% and a specificity of 84.6%, a performance that was significantly higher than that of the 30° × 30° classifer (p = 0.037). Artificial intelligence can accurately distinguish between atrophy caused by AMD or by EMAP on FAF images. Its performance are improved using wide field acquisitions.
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Affiliation(s)
- Maxime Chouraqui
- Department of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, 40, Avenue de Verdun, 94100, Créteil, France
| | - Emanuele Crincoli
- Department of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, 40, Avenue de Verdun, 94100, Créteil, France
- Catholic University of "Sacro Cuore", Rome, Italy
| | - Alexandra Miere
- Department of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, 40, Avenue de Verdun, 94100, Créteil, France.
| | - Isabelle Anne Meunier
- National Reference Center for Inherited Sensory Diseases, University Hospital of Montpellier, University of Montpellier, Montpellier, France
- Sensgene Care Network, Strasbourg, France
- Institute for Neurosciences of Montpellier, Inserm, University of Montpellier, Montpellier, France
| | - Eric H Souied
- Department of Ophthalmology, Centre Hospitalier Intercommunal de Créteil, 40, Avenue de Verdun, 94100, Créteil, France
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18
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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] [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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19
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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: 13] [Impact Index Per Article: 13.0] [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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20
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Singh RP, Amoaku W, Bandello F, Chen FK, Holz FG, Kodjikian L, Ruiz-Moreno JM, Joshi P, Wykoff CC. Diagnosis and Management of Patients With Geographic Atrophy Secondary to Age-Related Macular Degeneration: A Delphi Consensus Exercise. Ophthalmic Surg Lasers Imaging Retina 2023; 54:589-598. [PMID: 37847167 DOI: 10.3928/23258160-20230824-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
Abstract
Geographic atrophy (GA) is a progressive and irreversible retinal disease with no comprehensive recommendations for diagnosis or monitoring. We used a Delphi approach to determine consensus in key areas around diagnosis and management of GA. A steering committee of eight retina specialists developed two sequential online surveys administered to eye care professionals (ECPs). Consensus was defined as agreement by ≥ 75% of respondents. Up to 177 ECPs from eight countries completed one or both surveys. Consensus was achieved in several topics related to diagnostic imaging, including the use of optical coherence tomography, and the urgent need for treatments and beneficial interventions to reduce the associated burden. Currently, low-vision aids and smoking cessation are considered the most beneficial interventions. We demonstrate consensus for diagnosis and management of patients with GA including best practices in patient identification and monitoring, and unmet needs. [Ophthalmic Surg Lasers Imaging Retina 2023;54:589-598.].
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21
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Rosenfeld PJ, Cheng Y, Shen M, Gregori G, Wang RK. Unleashing the power of optical attenuation coefficients to facilitate segmentation strategies in OCT imaging of age-related macular degeneration: perspective. BIOMEDICAL OPTICS EXPRESS 2023; 14:4947-4963. [PMID: 37791280 PMCID: PMC10545179 DOI: 10.1364/boe.496080] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 10/05/2023]
Abstract
The use of optical attenuation coefficients (OAC) in optical coherence tomography (OCT) imaging of the retina has improved the segmentation of anatomic layers compared with traditional intensity-based algorithms. Optical attenuation correction has improved our ability to measure the choroidal thickness and choroidal vascularity index using dense volume scans. Algorithms that combine conventional intensity-based segmentation with depth-resolved OAC OCT imaging have been used to detect elevations of the retinal pigment epithelium (RPE) due to drusen and basal laminar deposits, the location of hyperpigmentation within the retina and along the RPE, the identification of macular atrophy, the thickness of the outer retinal (photoreceptor) layer, and the presence of calcified drusen. OAC OCT algorithms can identify the risk-factors that predict disease progression in age-related macular degeneration.
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Affiliation(s)
- Philip J. Rosenfeld
- Department of Ophthalmology, Bascom Palmer
Eye Institute, University of Miami Miller School of
Medicine, Miami, Florida, USA
| | - Yuxuan Cheng
- Department of Bioengineering,
University of Washington, Seattle,
Washington, USA
| | - Mengxi Shen
- Department of Ophthalmology, Bascom Palmer
Eye Institute, University of Miami Miller School of
Medicine, Miami, Florida, USA
| | - Giovanni Gregori
- Department of Ophthalmology, Bascom Palmer
Eye Institute, University of Miami Miller School of
Medicine, Miami, Florida, USA
| | - Ruikang K. Wang
- Department of Bioengineering,
University of Washington, Seattle,
Washington, USA
- Department of Ophthalmology,
University of Washington, Seattle,
Washington, USA
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22
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Wei W, Southern J, Zhu K, Li Y, Cordeiro MF, Veselkov K. Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography. Sci Rep 2023; 13:8296. [PMID: 37217770 DOI: 10.1038/s41598-023-35414-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 05/17/2023] [Indexed: 05/24/2023] Open
Abstract
Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has achieved a mean dice similarity coefficient score of 0.706 ± 0.039, a mean Precision score of 0.834 ± 0.048, and a mean Sensitivity score of 0.615 ± 0.051. These results show the unique potential of using artificially intelligence-aided methods for early detection and identification of the progression of MA in wet AMD, which can further support and assist clinical decisions.
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Affiliation(s)
- Wei Wei
- Department of Surgery and Cancer, Imperial College London, London, UK
- Ningbo Medical Center Lihuili Hospital, Ningbo, China
- Imperial College Ophthalmology Research Group, London, UK
| | | | - Kexuan Zhu
- Ningbo Medical Center Lihuili Hospital, Ningbo, China
| | - Yefeng Li
- School of Cyber Science and Engineering, Ningbo University of Technology, Ningbo, China
| | - Maria Francesca Cordeiro
- Department of Surgery and Cancer, Imperial College London, London, UK.
- Imperial College Ophthalmology Research Group, London, UK.
| | - Kirill Veselkov
- Department of Surgery and Cancer, Imperial College London, London, UK.
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23
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Wei W, Anantharanjit R, Patel RP, Cordeiro MF. Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence. Expert Rev Mol Diagn 2023:1-10. [PMID: 37144908 DOI: 10.1080/14737159.2023.2208751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
INTRODUCTION Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development. AREAS COVERED Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018. EXPERT OPINION There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.
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Affiliation(s)
- Wei Wei
- Department of Ophthalmology, Ningbo Medical Center Lihuili Hospital, Ningbo, China
- Department of Surgery & Cancer, Imperial College London, London, UK
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
| | - Rajeevan Anantharanjit
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
| | - Radhika Pooja Patel
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
| | - Maria Francesca Cordeiro
- Department of Surgery & Cancer, Imperial College London, London, UK
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Ophthalmology Research Group, London, UK
- Western Eye Hospital, Imperial College Healthcare NHS trust, London, UK
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24
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Mai J, Lachinov D, Riedl S, Reiter GS, Vogl WD, Bogunovic H, Schmidt-Erfurth U. Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment. Sci Rep 2023; 13:7028. [PMID: 37120456 PMCID: PMC10148818 DOI: 10.1038/s41598-023-34139-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/25/2023] [Indexed: 05/01/2023] Open
Abstract
Geographic atrophy (GA) represents a late stage of age-related macular degeneration, which leads to irreversible vision loss. With the first successful therapeutic approach, namely complement inhibition, huge numbers of patients will have to be monitored regularly. Given these perspectives, a strong need for automated GA segmentation has evolved. The main purpose of this study was the clinical validation of an artificial intelligence (AI)-based algorithm to segment a topographic 2D GA area on a 3D optical coherence tomography (OCT) volume, and to evaluate its potential for AI-based monitoring of GA progression under complement-targeted treatment. 100 GA patients from routine clinical care at the Medical University of Vienna for internal validation and 113 patients from the FILLY phase 2 clinical trial for external validation were included. Mean Dice Similarity Coefficient (DSC) was 0.86 ± 0.12 and 0.91 ± 0.05 for total GA area on the internal and external validation, respectively. Mean DSC for the GA growth area at month 12 on the external test set was 0.46 ± 0.16. Importantly, the automated segmentation by the algorithm corresponded to the outcome of the original FILLY trial measured manually on fundus autofluorescence. The proposed AI approach can reliably segment GA area on OCT with high accuracy. The availability of such tools represents an important step towards AI-based monitoring of GA progression under treatment on OCT for clinical management as well as regulatory trials.
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Affiliation(s)
- Julia Mai
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Dmitrii Lachinov
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Sophie Riedl
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Gregor S Reiter
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Wolf-Dieter Vogl
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
| | - Hrvoje Bogunovic
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria
| | - Ursula Schmidt-Erfurth
- Laboratory for Ophthalmic Image Analysis (OPTIMA), Department of Ophthalmology and Optometry, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
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25
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Chiang JN, Corradetti G, Nittala MG, Corvi F, Rakocz N, Rudas A, Durmus B, An U, Sankararaman S, Chiu A, Halperin E, Sadda SR. Automated Identification of Incomplete and Complete Retinal Epithelial Pigment and Outer Retinal Atrophy Using Machine Learning. Ophthalmol Retina 2023; 7:118-126. [PMID: 35995411 DOI: 10.1016/j.oret.2022.08.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/11/2022] [Accepted: 08/15/2022] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To assess and validate a deep learning algorithm to automatically detect incomplete retinal pigment epithelial and outer retinal atrophy (iRORA) and complete retinal pigment epithelial and outer retinal atrophy (cRORA) in eyes with age-related macular degeneration. DESIGN In a retrospective machine learning analysis, a deep learning model was trained to jointly classify the presence of iRORA and cRORA within a given B-scan. The algorithm was evaluated using 2 separate and independent datasets. PARTICIPANTS OCT B-scan volumes from 71 patients with nonneovascular age-related macular degeneration captured at the Doheny-University of California Los Angeles Eye Centers and the following 2 external OCT B-scans testing datasets: (1) University of Pennsylvania, University of Miami, and Case Western Reserve University and (2) Doheny Image Reading Research Laboratory. METHODS The images were annotated by an experienced grader for the presence of iRORA and cRORA. A Resnet18 model was trained to classify these annotations for each B-scan using OCT volumes collected at the Doheny-University of California Los Angeles Eye Centers. The model was applied to 2 testing datasets to assess out-of-sample model performance. MAIN OUTCOMES MEASURES Model performance was quantified in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC). Sensitivity, specificity, and positive predictive value were also compared against additional clinician annotators. RESULTS On an independently collected test set, consisting of 1117 volumes from the general population, the model predicted iRORA and cRORA presence within the entire volume with nearly perfect AUROC performance and AUPRC scores (iRORA, 0.61; 95% confidence interval [CI] [0.45, 0.82]: cRORA, 0.83; 95% CI [0.68, 0.95]). On another independently collected set, consisting of 60 OCT B-scans enriched for iRORA and cRORA lesions, the model performed with AUROC (iRORA: 0.68, 95% CI [0.54, 0.81]; cRORA: 0.84, 95% CI [0.75, 0.94]) and AUPRC (iRORA: 0.70, 95% CI [0.55, 0.86]; cRORA: 0.82, 95% CI [0.70, 0.93]). CONCLUSIONS A deep learning model can accurately and precisely identify both iRORA and cRORA lesions within the OCT B-scan volume. The model can achieve similar sensitivity compared with human graders, which potentially obviates a laborious and time-consuming annotation process and could be developed into a diagnostic screening tool.
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Affiliation(s)
- Jeffrey N Chiang
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, California
| | - Giulia Corradetti
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California
| | | | - Federico Corvi
- Doheny Eye Institute, Pasadena, California; Eye Clinic, Department of Biomedical and Clinical Science "Luigi Sacco,"," Sacco Hospital, University of Milan, Milan, Italy
| | - Nadav Rakocz
- Department of Computer Science, University of California Los Angeles, Los Angeles, California
| | - Akos Rudas
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, California
| | - Berkin Durmus
- Department of Computer Science, University of California Los Angeles, Los Angeles, California
| | - Ulzee An
- Department of Computer Science, University of California Los Angeles, Los Angeles, California
| | - Sriram Sankararaman
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, California; Department of Computer Science, University of California Los Angeles, Los Angeles, California; Department of Human Genetics, University of California Los Angeles, Los Angeles, California
| | - Alec Chiu
- Department of Computer Science, University of California Los Angeles, Los Angeles, California
| | - Eran Halperin
- Department of Computational Medicine, University of California Los Angeles, Los Angeles, California; Department of Computer Science, University of California Los Angeles, Los Angeles, California; Department of Human Genetics, University of California Los Angeles, Los Angeles, California; Department of Anesthesiology, University of California Los Angeles, Los Angeles, California; Institute of Precision Health, University of California Los Angeles, Los Angeles, California
| | - Srinivas R Sadda
- Doheny Eye Institute, Pasadena, California; Department of Ophthalmology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California.
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TSSK-Net: Weakly supervised biomarker localization and segmentation with image-level annotation in retinal OCT images. Comput Biol Med 2023; 153:106467. [PMID: 36584602 DOI: 10.1016/j.compbiomed.2022.106467] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 11/16/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
The localization and segmentation of biomarkers in OCT images are critical steps in retina-related disease diagnosis. Although fully supervised deep learning models can segment pathological regions, their performance relies on labor-intensive pixel-level annotations. Compared with dense pixel-level annotation, image-level annotation can reduce the burden of manual annotation. Existing methods for image-level annotation are usually based on class activation maps (CAM). However, current methods still suffer from model collapse, training instability, and anatomical mismatch due to the considerable variation in retinal biomarkers' shape, texture, and size. This paper proposes a novel weakly supervised biomarkers localization and segmentation method, requiring only image-level annotations. The technique is a Teacher-Student network with joint Self-supervised contrastive learning and Knowledge distillation-based anomaly localization, namely TSSK-Net. Specifically, we treat retinal biomarker regions as abnormal regions distinct from normal regions. First, we propose a novel pre-training strategy based on supervised contrastive learning that encourages the model to learn the anatomical structure of normal OCT images. Second, we design a fine-tuning module and propose a novel hybrid network structure. The network includes supervised contrastive loss for feature learning and cross-entropy loss for classification learning. To further improve the performance, we propose an efficient strategy to combine these two losses to preserve the anatomical structure and enhance the encoding representation of features. Finally, we design a knowledge distillation-based anomaly segmentation method that is effectively combined with the previous model to alleviate the challenge of insufficient supervision. Experimental results on a local dataset and a public dataset demonstrated the effectiveness of our proposed method. Our proposed method can effectively reduce the annotation burden of ophthalmologists in OCT images.
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iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis. J Clin Med 2023; 12:jcm12020400. [PMID: 36675327 PMCID: PMC9862104 DOI: 10.3390/jcm12020400] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/29/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Background: Epiretinal membranes (ERM) have been found to be common among individuals >50 years old. However, the severity grading assessment for ERM based on optical coherence tomography (OCT) images has remained a challenge due to lacking reliable and interpretable analysis methods. Thus, this study aimed to develop a two-stage deep learning (DL) system named iERM to provide accurate automatic grading of ERM for clinical practice. Methods: The iERM was trained based on human segmentation of key features to improve classification performance and simultaneously provide interpretability to the classification results. We developed and tested iERM using a total of 4547 OCT B-Scans of four different commercial OCT devices that were collected from nine international medical centers. Results: As per the results, the integrated network effectively improved the grading performance by 1−5.9% compared with the traditional classification DL model and achieved high accuracy scores of 82.9%, 87.0%, and 79.4% in the internal test dataset and two external test datasets, respectively. This is comparable to retinal specialists whose average accuracy scores are 87.8% and 79.4% in two external test datasets. Conclusion: This study proved to be a benchmark method to improve the performance and enhance the interpretability of the traditional DL model with the implementation of segmentation based on prior human knowledge. It may have the potential to provide precise guidance for ERM diagnosis and treatment.
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Machine Learning-Based Automated Detection and Quantification of Geographic Atrophy and Hypertransmission Defects Using Spectral Domain Optical Coherence Tomography. J Pers Med 2022; 13:jpm13010037. [PMID: 36675697 PMCID: PMC9861976 DOI: 10.3390/jpm13010037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/19/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
The current study describes the development and assessment of innovative, machine learning (ML)-based approaches for automated detection and pixel-accurate measurements of regions with geographic atrophy (GA) in late-stage age-related macular degeneration (AMD) using optical coherence tomography systems. 900 OCT volumes, 100266 B-scans, and en face OCT images from 341 non-exudative AMD patients with or without GA were included in this study from both Cirrus (Zeiss) and Spectralis (Heidelberg) OCT systems. B-scan and en face level ground truth GA masks were created on OCT B-scan where the segmented ellipsoid zone (EZ) line, retinal pigment epithelium (RPE) line, and bruchs membrane (BM) line overlapped. Two deep learning-based approaches, B-scan level and en face level, were trained. The OCT B-scan model had detection accuracy of 91% and GA area measurement accuracy of 94%. The en face OCT model had detection accuracy of 82% and GA area measurement accuracy of 96% with primary target of hypertransmission on en face OCT. Accuracy was good for both devices tested (92-97%). Automated lesion size stratification for CAM cRORA definition of 250um minimum lesion size was feasible. High-performance models for automatic detection and segmentation of GA area were achieved using OCT systems and deep learning. The automatic measurements showed high correlation with the ground truth. The en face model excelled at identification of hypertransmission defects. The models performance generalized well across device types tested. Future development will include integration of both models to enhance feature detection across GA lesions as well as isolating hypertransmission defects without GA for pre-GA biomarker extraction.
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Wang J, Wang Z, Chen M, Xiao Y, Chen S, Wu L, Yao L, Jiang X, Li J, Xu M, Lin M, Zhu Y, Luo R, Zhang C, Li X, Yu H. An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding. NPJ Digit Med 2022; 5:183. [PMID: 36536039 PMCID: PMC9763258 DOI: 10.1038/s41746-022-00729-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
Bleeding risk factors for gastroesophageal varices (GEV) detected by endoscopy in cirrhotic patients determine the prophylactical treatment patients will undergo in the following 2 years. We propose a methodology for measuring the risk factors. We create an artificial intelligence system (ENDOANGEL-GEV) containing six models to segment GEV and to classify the grades (grades 1-3) and red color signs (RC, RC0-RC3) of varices. It also summarizes changes in the above results with region in real time. ENDOANGEL-GEV is trained using 6034 images from 1156 cirrhotic patients across three hospitals (dataset 1) and validated on multicenter datasets with 11009 images from 141 videos (dataset 2) and in a prospective study recruiting 161 cirrhotic patients from Renmin Hospital of Wuhan University (dataset 3). In dataset 1, ENDOANGEL-GEV achieves intersection over union values of 0.8087 for segmenting esophageal varices and 0.8141 for gastric varices. In dataset 2, the system maintains fairly accuracy across images from three hospitals. In dataset 3, ENDOANGEL-GEV surpasses attended endoscopists in detecting RC of GEV and classifying grades (p < 0.001). When ranking the risk of patients combined with the Child‒Pugh score, ENDOANGEL-GEV outperforms endoscopists for esophageal varices (p < 0.001) and shows comparable performance for gastric varices (p = 0.152). Compared with endoscopists, ENDOANGEL-GEV may help 12.31% (16/130) more patients receive the right intervention. We establish an interpretable system for the endoscopic diagnosis and risk stratification of GEV. It will assist in detecting the first bleeding risk factors accurately and expanding the scope of quantitative measurement of diseases.
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Affiliation(s)
- Jing Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Zhengqiang Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingkai Chen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yong Xiao
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shi Chen
- Department of Gastroenterology, Wuhan Puren Hospital, Wuhan, China
| | - Lianlian Wu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Liwen Yao
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xiaoda Jiang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Jiao Li
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Ming Xu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mengjuan Lin
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yijie Zhu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Renquan Luo
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chenxia Zhang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Xun Li
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
| | - Honggang Yu
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China ,grid.412632.00000 0004 1758 2270Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
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Ibragimova RR, Gilmanov II, Lopukhova EA, Lakman IA, Bilyalov AR, Mukhamadeev TR, Kutluyarov RV, Idrisova GM. Algorithm of segmentation of OCT macular images to analyze the results in patients with age-related macular degeneration. BULLETIN OF RUSSIAN STATE MEDICAL UNIVERSITY 2022. [DOI: 10.24075/brsmu.2022.062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Age-related macular degeneration (AMD) is one of the main causes of loss of sight and hypovision in people over working age. Results of optical coherence tomography (OCT) are essential for diagnostics of the disease. Developing the recommendation system to analyze OCT images will reduce the time to process visual data and decrease the probability of errors while working as a doctor. The purpose of the study was to develop an algorithm of segmentation to analyze the results of macular OCT in patients with AMD. It allows to provide a correct prediction of an AMD stage based on the form of discovered pathologies. A program has been developed in the Python programming language using the Pytorch and TensorFlow libraries. Its quality was estimated using OCT macular images of 51 patients with early, intermediate, late AMD. A segmentation algorithm of OCT images was developed based on convolutional neural network. UNet network was selected as architecture of high-accuracy neural net. The neural net is trained on macular OCT images of 125 patients (197 eyes). The author algorithm displayed 98.1% of properly segmented areas on OCT images, which are the most essential for diagnostics and determination of an AMD stage. Weighted sensitivity and specificity of AMD stage classifier amounted to 83.8% and 84.9% respectively. The developed algorithm is promising as a recommendation system that implements the AMD classification based on data that promote taking decisions regarding the treatment strategy.
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Affiliation(s)
| | - II Gilmanov
- Ufa State Aviation Technical University, Ufa, Russia
| | - EA Lopukhova
- Ufa State Aviation Technical University, Ufa, Russia
| | - IA Lakman
- Bashkir State Medical University, Ufa, Russia
| | - AR Bilyalov
- Bashkir State Medical University, Ufa, Russia
| | | | - RV Kutluyarov
- Ufa State Aviation Technical University, Ufa, Russia
| | - GM Idrisova
- Bashkir State Medical University, Ufa, Russia
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Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2022; 2:100078. [PMID: 37846285 PMCID: PMC10577833 DOI: 10.1016/j.aopr.2022.100078] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/01/2022] [Accepted: 08/18/2022] [Indexed: 10/18/2023]
Abstract
Background The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.
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Affiliation(s)
- Kai Jin
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Research Trends and Hotspots of Retinal Optical Coherence Tomography: A 31-Year Bibliometric Analysis. J Clin Med 2022; 11:jcm11195604. [PMID: 36233468 PMCID: PMC9572389 DOI: 10.3390/jcm11195604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/12/2022] [Accepted: 09/19/2022] [Indexed: 11/17/2022] Open
Abstract
The emergence of optical coherence tomography (OCT) over the past three decades has sparked great interest in retinal research. However, a comprehensive analysis of the trends and hotspots in retinal OCT research is currently lacking. We searched the publications on retinal OCT in the Web of Science database from 1991 to 2021 and performed the co-occurrence keyword analysis and co-cited reference network using bibliometric tools. A total of 25,175 publications were included. There has been a progressive increase in the number of publications. The keyword co-occurrence network revealed five clusters of hotspots: (1) thickness measurements; (2) therapies for macular degeneration and macular edema; (3) degenerative retinal diseases; (4) OCT angiography (OCTA); and (5) vitrectomy for macular hole and epiretinal membrane. The co-citation analysis displayed 26 highly credible clusters (S = 0.9387) with a well-structured network (Q = 0.879). The major trends of research were: (1) thickness measurements; (2) therapies for macular degeneration and macular edema; and (3) OCTA. Recent emerging frontiers showed a growing interest in OCTA, vessel density, choriocapillaris, central serous chorioretinopathy, Alzheimer’s disease, and deep learning. This review summarized 31 years of retinal OCT research, shedding light on the hotspots, main themes, and emerging frontiers to assist in future research.
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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: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [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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Ong J, Zarnegar A, Corradetti G, Singh SR, Chhablani J. Advances in Optical Coherence Tomography Imaging Technology and Techniques for Choroidal and Retinal Disorders. J Clin Med 2022; 11:jcm11175139. [PMID: 36079077 PMCID: PMC9457394 DOI: 10.3390/jcm11175139] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 08/27/2022] [Accepted: 08/28/2022] [Indexed: 11/16/2022] Open
Abstract
Optical coherence tomography (OCT) imaging has played a pivotal role in the field of retina. This light-based, non-invasive imaging modality provides high-quality, cross-sectional analysis of the retina and has revolutionized the diagnosis and management of retinal and choroidal diseases. Since its introduction in the early 1990s, OCT technology has continued to advance to provide quicker acquisition times and higher resolution. In this manuscript, we discuss some of the most recent advances in OCT technology and techniques for choroidal and retinal diseases. The emerging innovations discussed include wide-field OCT, adaptive optics OCT, polarization sensitive OCT, full-field OCT, hand-held OCT, intraoperative OCT, at-home OCT, and more. The applications of these rising OCT systems and techniques will allow for a closer monitoring of chorioretinal diseases and treatment response, more robust analysis in basic science research, and further insights into surgical management. In addition, these innovations to optimize visualization of the choroid and retina offer a promising future for advancing our understanding of the pathophysiology of chorioretinal diseases.
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Affiliation(s)
- Joshua Ong
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Arman Zarnegar
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Giulia Corradetti
- Department of Ophthalmology, Doheny Eye Institute, Los Angeles, CA 90095, USA
- Stein Eye Institute, David Geffen School of Medicine at the University of California, Los Angeles, CA 90033, USA
| | | | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Correspondence:
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Machine learning-based 3D modeling and volumetry of human posterior vitreous cavity of optical coherence tomographic images. Sci Rep 2022; 12:13836. [PMID: 35974072 PMCID: PMC9381727 DOI: 10.1038/s41598-022-17615-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 07/28/2022] [Indexed: 11/12/2022] Open
Abstract
The structure of the human vitreous varies considerably because of age-related liquefactions of the vitreous gel. These changes are poorly studied in vivo mainly because their high transparency and mobility make it difficult to obtain reliable and repeatable images of the vitreous. Optical coherence tomography can detect the boundaries between the vitreous gel and vitreous fluid, but it is difficult to obtain high resolution images that can be used to convert the images to three-dimensional (3D) images. Thus, the purpose of this study was to determine the shape and characteristics of the vitreous fluid using machine learning-based 3D modeling in which manually labelled fluid areas were used to train deep convolutional neural network (DCNN). The trained DCNN labelled vitreous fluid automatically and allowed us to obtain 3D vitreous model and to quantify the vitreous fluidic cavities. The mean volume and surface area of posterior vitreous fluidic cavities are 19.6 ± 7.8 mm3 and 104.0 ± 18.9 mm2 in eyes of 17 school children. The results suggested that vitreous fluidic cavities expanded as the cavities connects with each other, and this modeling system provided novel imaging markers for aging and eye diseases.
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Amygdalos I, Hachgenei E, Burkl L, Vargas D, Goßmann P, Wolff LI, Druzenko M, Frye M, König N, Schmitt RH, Chrysos A, Jöchle K, Ulmer TF, Lambertz A, Knüchel-Clarke R, Neumann UP, Lang SA. Optical coherence tomography and convolutional neural networks can differentiate colorectal liver metastases from liver parenchyma ex vivo. J Cancer Res Clin Oncol 2022:10.1007/s00432-022-04263-z. [PMID: 35960377 DOI: 10.1007/s00432-022-04263-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 08/02/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Optical coherence tomography (OCT) is an imaging technology based on low-coherence interferometry, which provides non-invasive, high-resolution cross-sectional images of biological tissues. A potential clinical application is the intraoperative examination of resection margins, as a real-time adjunct to histological examination. In this ex vivo study, we investigated the ability of OCT to differentiate colorectal liver metastases (CRLM) from healthy liver parenchyma, when combined with convolutional neural networks (CNN). METHODS Between June and August 2020, consecutive adult patients undergoing elective liver resections for CRLM were included in this study. Fresh resection specimens were scanned ex vivo, before fixation in formalin, using a table-top OCT device at 1310 nm wavelength. Scanned areas were marked and histologically examined. A pre-trained CNN (Xception) was used to match OCT scans to their corresponding histological diagnoses. To validate the results, a stratified k-fold cross-validation (CV) was carried out. RESULTS A total of 26 scans (containing approx. 26,500 images in total) were obtained from 15 patients. Of these, 13 were of normal liver parenchyma and 13 of CRLM. The CNN distinguished CRLM from healthy liver parenchyma with an F1-score of 0.93 (0.03), and a sensitivity and specificity of 0.94 (0.04) and 0.93 (0.04), respectively. CONCLUSION Optical coherence tomography combined with CNN can distinguish between healthy liver and CRLM with great accuracy ex vivo. Further studies are needed to improve upon these results and develop in vivo diagnostic technologies, such as intraoperative scanning of resection margins.
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Affiliation(s)
- Iakovos Amygdalos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany.
| | - Enno Hachgenei
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Luisa Burkl
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - David Vargas
- Institute for Histopathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Paul Goßmann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Laura I Wolff
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Mariia Druzenko
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Maik Frye
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Niels König
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany
| | - Robert H Schmitt
- Department of Production Metrology, Fraunhofer Institute for Production Technology IPT, Aachen, Germany.,Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
| | - Alexandros Chrysos
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Katharina Jöchle
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Tom F Ulmer
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Andreas Lambertz
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ruth Knüchel-Clarke
- Institute for Histopathology, University Hospital RWTH Aachen, Aachen, Germany
| | - Ulf P Neumann
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Sven A Lang
- Department of General, Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
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Khan NC, Perera C, Dow ER, Chen KM, Mahajan VB, Mruthyunjaya P, Do DV, Leng T, Myung D. Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models. Diagnostics (Basel) 2022; 12:diagnostics12071714. [PMID: 35885619 PMCID: PMC9322827 DOI: 10.3390/diagnostics12071714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/23/2022] [Accepted: 06/24/2022] [Indexed: 12/02/2022] Open
Abstract
While color fundus photos are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that ocular imaging contains valuable information regarding the systemic health features of patients. These features can be identified through computer vision techniques including deep learning (DL) artificial intelligence (AI) models. We aim to construct a DL model that can predict systemic features from fundus images and to determine the optimal method of model construction for this task. Data were collected from a cohort of patients undergoing diabetic retinopathy screening between March 2020 and March 2021. Two models were created for each of 12 systemic health features based on the DenseNet201 architecture: one utilizing transfer learning with images from ImageNet and another from 35,126 fundus images. Here, 1277 fundus images were used to train the AI models. Area under the receiver operating characteristics curve (AUROC) scores were used to compare the model performance. Models utilizing the ImageNet transfer learning data were superior to those using retinal images for transfer learning (mean AUROC 0.78 vs. 0.65, p-value < 0.001). Models using ImageNet pretraining were able to predict systemic features including ethnicity (AUROC 0.93), age > 70 (AUROC 0.90), gender (AUROC 0.85), ACE inhibitor (AUROC 0.82), and ARB medication use (AUROC 0.78). We conclude that fundus images contain valuable information about the systemic characteristics of a patient. To optimize DL model performance, we recommend that even domain specific models consider using transfer learning from more generalized image sets to improve accuracy.
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Affiliation(s)
- Nergis C. Khan
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Chandrashan Perera
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
- Department of Ophthalmology, Fremantle Hospital, Perth, WA 6004, Australia
| | - Eliot R. Dow
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Karen M. Chen
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Vinit B. Mahajan
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Prithvi Mruthyunjaya
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Diana V. Do
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - Theodore Leng
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
| | - David Myung
- Byers Eye Institute at Stanford, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, CA 94305, USA; (N.C.K.); (C.P.); (E.R.D.); (K.M.C.); (V.B.M.); (P.M.); (D.V.D.); (T.L.)
- VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Correspondence: ; Tel.: +1-650-724-3948
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Wu Z, Kumar H, Hodgson LAB, Guymer RH. Reticular Pseudodrusen on the Risk of Progression in Intermediate Age-Related Macular Degeneration. Am J Ophthalmol 2022; 239:202-211. [PMID: 35288077 DOI: 10.1016/j.ajo.2022.03.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 03/01/2022] [Accepted: 03/03/2022] [Indexed: 11/28/2022]
Abstract
PURPOSE To examine the association between reticular pseudodrusen (RPD) and progression to late age-related macular degeneration (AMD) in individuals with intermediate AMD. DESIGN Prospective cohort study. METHODS Two hundred eighty eyes from 140 participants with bilateral large drusen underwent multimodal imaging (MMI), including optical coherence tomography (OCT), near-infrared reflectance (NIR), fundus autofluorescence, and color fundus photography (CFP), at 6-monthly intervals up over a 36-month follow-up period. The presence of RPD per eye was determined based on either a combined MMI criterion, or each individual imaging modality, and their extent measured on combined OCT and NIR imaging. The association between the presence of RPD on different imaging modalities, and their extent, with the development of late AMD (including OCT-defined atrophy) was evaluated. RESULTS The presence of RPD on MMI, or any of its individual modalities, at baseline was not significantly associated with an increased rate of developing late AMD, with or without adjusting for risk factors for AMD progression (age, drusen volume on OCT, and pigmentary abnormalities on CFP; all P ≥ 0.205). The extent of RPD present was also not significantly associated with an increased rate of developing late AMD, with or without adjustment for risk factors for AMD progression (both P ≥ 0.522). CONCLUSIONS In this cohort with bilateral large drusen, the presence of RPD was not significantly associated with an increased risk of developing late AMD. Additional longitudinal studies in all stages of AMD are needed to understand the implications of RPD on vision loss in this condition.
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Affiliation(s)
- Zhichao Wu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital (Z.W., H.K., L.A.B.H., R.H.G.), East Melbourne, Australia; Ophthalmology, Department of Surgery, the University of Melbourne (Z.W., H.K., R.H.G.), Melbourne, Victoria, Australia.
| | - Himeesh Kumar
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital (Z.W., H.K., L.A.B.H., R.H.G.), East Melbourne, Australia; Ophthalmology, Department of Surgery, the University of Melbourne (Z.W., H.K., R.H.G.), Melbourne, Victoria, Australia
| | - Lauren A B Hodgson
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital (Z.W., H.K., L.A.B.H., R.H.G.), East Melbourne, Australia
| | - Robyn H Guymer
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital (Z.W., H.K., L.A.B.H., R.H.G.), East Melbourne, Australia; Ophthalmology, Department of Surgery, the University of Melbourne (Z.W., H.K., R.H.G.), Melbourne, Victoria, Australia
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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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Dow ER, Keenan TDL, Lad EM, Lee AY, Lee CS, Loewenstein A, Eydelman MB, Chew EY, Keane PA, Lim JI. From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration. Ophthalmology 2022; 129:e43-e59. [PMID: 35016892 PMCID: PMC9859710 DOI: 10.1016/j.ophtha.2022.01.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/16/2021] [Accepted: 01/04/2022] [Indexed: 01/25/2023] Open
Abstract
OBJECTIVE Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD. PURPOSE To delineate the state of AI for AMD, including current data, standards, achievements, and challenges. METHODS Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus. RESULTS Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization. CONCLUSIONS Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.
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Affiliation(s)
- Eliot R Dow
- Byers Eye Institute, Stanford University, Palo Alto, California
| | - Tiarnan D L Keenan
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Eleonora M Lad
- Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, Washington
| | - Anat Loewenstein
- Division of Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Malvina B Eydelman
- Office of Health Technology 1, Center of Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland
| | - Emily Y Chew
- Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
| | - Pearse A Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom.
| | - Jennifer I Lim
- Department of Ophthalmology, University of Illinois at Chicago, Chicago, Illinois.
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Rahman L, Hafejee A, Anantharanjit R, Wei W, Cordeiro MF. Accelerating precision ophthalmology: recent advances. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2022. [DOI: 10.1080/23808993.2022.2154146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Loay Rahman
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Ammaarah Hafejee
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Rajeevan Anantharanjit
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
| | - Wei Wei
- Imperial College Ophthalmology Research Group (ICORG), Imperial College Healthcare NHS Trust, London, UK
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, UK
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Deep learning in geographic atrophy: the best is yet to come. Lancet Digit Health 2021; 3:e617-e618. [PMID: 34509422 DOI: 10.1016/s2589-7500(21)00204-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 08/09/2021] [Indexed: 11/23/2022]
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