Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes
Open accessAbstract
Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures: Use of a deep learning system. Main Outcomes and Measures: Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results: In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance: In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.
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Images obtained during diabetic retinopathy screening can be non-gradable due to poor image quality, patient movement, improper equipment use, or ocular media opacities such as cataracts.
“Images obtained during DR screening can be non-gradable due to poor image quality, patient movement, improper equipment use, or ocular media opacities such as cataracts [42].”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [42]
The Definition of Referable Diabetic Retinopathy section says a photograph was ungradable if more than one-third was obscured. The Discussion also describes poorer-quality images, including ungradable ones, in a diabetic retinopathy screening program. Neither passage identifies patient movement, improper equipment use, cataracts, or other ocular media opacities as causes; page numbers are unavailable in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredUK and Australia recommendations suggested a minimum sensitivity of either 80% or 60%, and a specificity of at least 95%.
“In the UK and Australia, previous recommendations suggested a minimum sensitivity of either 80% or 60%, and a specificity of at least 95%25–30.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [30]
The Statistical Analysis section (page unavailable) reports minimum sensitivity recommendations of 60% in Australia and 80% in the UK for diabetic retinopathy screening. It does not state that either country recommended specificity of at least 95%; the specificity figures elsewhere in the paper describe screening performance, not those recommendations. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAI-enabled diabetic retinopathy screening has progressed from retrospective validation to prospective and real-world deployment.
“Artificial intelligence (AI)-enabled retinal screening has progressed from retrospective validation to prospective and real-world deployment”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [7]
The study’s aims and Discussion (page unavailable) describe validation using images from an ongoing Singapore screening program and 10 external datasets, extending earlier database-based validation to clinical screening settings. The Discussion examines how the system could be deployed, but our copy does not establish prospective evaluation or actual real-world deployment. (LLM, conf=0.88)
Experimental — accuracy not yet measuredOphthalmology systems show strong results in diabetic retinopathy detection and screening.
“Dermatology studies demonstrate specialist-level classification of skin cancer [9], while ophthalmology systems show strong results in diabetic retinopathy detection and screening [10,11].”
Ting et al. reported that a CNN using retinal images for DR screening achieved an AUC of 0.936, sensitivity of 90.5%, and specificity of 91.6% for referable DR; and for vision-threatening DR, AUC was 0.958, sensitivity was 100%, and specificity was 91.1%.
“the AUC of the system for referable DR was 0.936, sensitivity was 90.5%, and specificity was 91.6%; for vision-threatening DR, AUC was 0.958, sensitivity was 100%, and specificity was 91.1%”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as Ting et al.107
AI performance extended to multiethnic retinal image datasets across DR and other ocular conditions.
“Ting and colleagues extended this work to multiethnic retinal image datasets and demonstrated performance across DR and other ocular conditions (7).”
From From validation to public health impact: implementing equitable AI-enabled diabetic retinopathy screening (2026) · cites this paper as [7]
Retinopathy screening programs work best in defined populations with government-organized health care, such as UK, Scandinavia, and American Indian tribes, not the general US population.
“Screening programs have been most successful in defined populations with government-organized health care systems, such as in the United Kingdom and Scandinavia and in American Indian tribes rather than in the general population of the United States.503–506”
From American Association of Clinical Endocrinology Clinical Practice Guideline: Developing a Diabetes Mellitus Comprehensive Care Plan-2022 Update. (2022) · cites this paper as [506]
The introduction describes diabetic-retinopathy screening as a blindness-prevention strategy, and the study examines images from Singapore’s national screening program. Our copy does not assess whether programs are most successful in government-organized health systems or compare the United Kingdom, Scandinavia, American Indian tribes, and the general US population (searched for those populations and the claimed comparison). (LLM, conf=0.76)
Experimental — accuracy not yet measuredWearable devices and remote monitoring systems provide continuous physiological data, enabling longitudinal risk prediction outside traditional clinical settings.
“Wearable devices and remote monitoring systems provide continuous physiological data, enabling longitudinal risk prediction outside traditional clinical settings [11].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [11]
The study aim and Statistical Analysis passages describe a deep-learning system that classifies retinal images for diabetic retinopathy and related eye diseases. Searches for wearable devices, remote monitoring, continuous physiological data, and longitudinal risk prediction found no passage addressing the citing sentence’s claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCNNs can diagnose or predict the progression of center-involved diabetic macular edema, age-related macular degeneration, glaucoma, manifest visual field loss, childhood blindness, and others.
“Similarly, they can diagnose or predict the progression of center-involved diabetic macular edema 109 , age-related macular degeneration 107,110 , glaucoma 107,111 , manifest visual field loss 112 , childhood blindness 113 , and others.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [107]
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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests
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Detection of anaemia from retinal fundus images via deep learning
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Regulation of predictive analytics in medicine
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What should medical students know about artificial intelligence in medicine?
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Self-citationDeep Learning and Glaucoma Specialists
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Deep Learning for Chest Radiograph Diagnosis in the Emergency Department
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Large language models and their impact in ophthalmology
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Self-citationInterpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks
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Visualizing Deep Learning Models for the Detection of Referable Diabetic Retinopathy and Glaucoma
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Self-citationArtificial intelligence promotes the diagnosis and screening of diabetic retinopathy
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Protecting Data Privacy in the Age of AI-Enabled Ophthalmology
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Cost-effectiveness of artificial intelligence screening for diabetic retinopathy in rural China
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The War on Diabetic Retinopathy: Where Are We Now?
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Self-citationDeep Learning Application for Effective Classification of Different Types of Psoriasis
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Artificial intelligence in radiology: who’s afraid of the big bad wolf?
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Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship
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Evaluating artificial intelligence applications in clinical settings
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Automated Diagnosis and Grading of Diabetic Retinopathy Using Optical Coherence Tomography
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Self-citationArtificial intelligence for ultrasonography: unique opportunities and challenges
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Automated and computer-assisted detection, classification, and diagnosis of diabetic retinopathy
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Self-citationExpert Discussions Improve Comprehension of Difficult Cases in Medical Image Assessment
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Remote Tool-Based Adjudication for Grading Diabetic Retinopathy
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Artificial Intelligence in Medicine
Malik, M., Tariq, M. I., Kamran, M., & Naqvi, M. R. (2021) · Smart Innovation, Systems and Technologies · doi:10.1007/978-981-16-1209-1_15 · Cited by 11 · see citing works
Diabetic retinopathy screening model in low and middle-income countries: a scoping review
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“The diagnostic capability of such systems has also been shown to be c omparable to trained human graders in multi-ethnic settings [44].”
The role of multimodality in clinical disease diagnosis: advances, challenges, and opportunities
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“Dermatology studies demonstrate specialist-level classification of skin cancer [9], while ophthalmology systems show strong results in diabetic retinopathy detection and screening [10,11].”
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Artificial intelligence and deep learning in ophthalmology
Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A. Y., Raman, R., Tan, G. S. W., Schmetterer, L., Keane, P. A., & Wong, T. Y. (2019) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2018-313173 · Cited by 1,554
Self-citation
- Authors
- Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., Wong, E. Y. M., Sabanayagam, C., Baskaran, M., Ibrahim, F., Tan, N. C., Finkelstein, E. A., Lamoureux, E. L., Wong, I. Y., Bressler, N. M., Sivaprasad, S., Varma, R., Jonas, J. B., He, M. G., Cheng, C.-Y., Cheung, G. C. M., Aung, T., Hsu, W., Lee, M. L., & Wong, T. Y.
- Affiliations
- National University of Singapore (SG)
- Chinese University of Hong Kong (HK)
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore
- Singapore National Eye Center (SG)
- Instituto Mexicano De Oftalmologia, IAP, Queretaro, Mexico.
- SingHealth (SG)
- University of Hong Kong (HK)
- Johns Hopkins Medicine (US)
- National Health Service (GB)
- University of Southern California (US)
- Heidelberg University (DE)
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yatsen University, Guangzhou, China
- Corresponding author
- Wong, Tien Yin
- ORCID
- Ting, Daniel Shu Wei
- Cheung, Carol Yim-Lui
- Lim, Gilbert
- Tan, Gavin Siew Wei
- Lee, Shu Yen
- Sabanayagam, Charumathi
- Baskaran, Mani
- Ibrahim, Farah
- Tan, Ngiap Chuan
- Finkelstein, Eric A.
- Lamoureux, Ecosse L.
- Bressler, Neil M.
- Sivaprasad, Sobha
- Varma, Rohit
- Jonas, Jost B.
- Cheng, Ching-Yu
- Aung, Tin
- Hsu, Wynne
- Lee, Mong Li
- Wong, Tien Yin
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2017-12-12
- Volume
- 318
- Issue
- 22
- Pages
- 2211
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC5820739
- OpenAlex
- W2772246530
- Cited by
- 2,402
- References
- 42
- Citations by year
- 2026: 315 · 2025: 286 · 2024: 327 · 2023: 294 · 2022: 311 · 2021: 301 · 2020: 266 · 2019: 207 · 2018: 87 · 2017: 2 · 2014: 1 · 2012: 1
- FWCI
- 117.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Johns Hopkins University
- Innovative Research Group Project of the National Natural Science Foundation of China
- Tanoto Foundation
- National Health Insurance Service
- SingHealth Foundation
- Medical Research Council
- National Medical Research Council (IRG09-nov014, 09/1/35/19/616, IRG07nov013, (#CG/SERI/2010, 1409022)
- Biomedical Research Council (IRG09nov014, IRG07nov013, CG/SERI/2010, 09/1/35/19/616)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Ophthalmology › Ophthalmology and Visual Health Research
- Keywords
- diabetic retinopathy, referable diabetic retinopathy, vision-threatening diabetic retinopathy, glaucoma, age-related macular degeneration, retinal image analysis, deep learning, automated disease detection, multi-ethnic population, diabetes mellitus, diabetic retinopathy screening, diagnostic accuracy, multi-ethnic cohort, non-proliferative diabetic retinopathy
- MeSH terms
- Machine Learning, Diabetes Mellitus, Diabetic Retinopathy, Eye Diseases, Female, Glaucoma, Humans, Male, Middle Aged, Retina, ROC Curve, Sensitivity and Specificity, Area Under Curve, Datasets as Topic
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