Cited by 304Retinal Imaging and AnalysisRetinal Diseases and TreatmentsOphthalmology and Visual Health Research

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Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

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Gulshan, Varun, Rajan, Renu P., Widner, Kasumi, Wu, Derek, Wubbels, Peter, Rhodes, Tyler, Whitehouse, Kira, Coram, Marc, Corrado, Greg, Ramasamy, Kim, Raman, Rajiv, Peng, Lily, Webster, Dale R.·2019·JAMA Ophthalmology

DOI 10.1001/jamaophthalmol.2019.2004PMC PMC6567842

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Abstract

IMPORTANCE: More than 60 million people in India have diabetes and are at risk for diabetic retinopathy (DR), a vision-threatening disease. Automated interpretation of retinal fundus photographs can help support and scale a robust screening program to detect DR. OBJECTIVE: To prospectively validate the performance of an automated DR system across 2 sites in India. DESIGN, SETTING, AND PARTICIPANTS: This prospective observational study was conducted at 2 eye care centers in India (Aravind Eye Hospital and Sankara Nethralaya) and included 3049 patients with diabetes. Data collection and patient enrollment took place between April 2016 and July 2016 at Aravind and May 2016 and April 2017 at Sankara Nethralaya. The model was trained and fixed in March 2016. INTERVENTIONS: Automated DR grading system compared with manual grading by 1 trained grader and 1 retina specialist from each site. Adjudication by a panel of 3 retinal specialists served as the reference standard in the cases of disagreement. MAIN OUTCOMES AND MEASURES: Sensitivity and specificity for moderate or worse DR or referable diabetic macula edema. RESULTS: Of 3049 patients, 1091 (35.8%) were women and the mean (SD) age for patients at Aravind and Sankara Nethralaya was 56.6 (9.0) years and 56.0 (10.0) years, respectively. For moderate or worse DR, the sensitivity and specificity for manual grading by individual nonadjudicator graders ranged from 73.4% to 89.8% and from 83.5% to 98.7%, respectively. The automated DR system's performance was equal to or exceeded manual grading, with an 88.9% sensitivity (95% CI, 85.8-91.5), 92.2% specificity (95% CI, 90.3-93.8), and an area under the curve of 0.963 on the data set from Aravind Eye Hospital and 92.1% sensitivity (95% CI, 90.1-93.8), 95.2% specificity (95% CI, 94.2-96.1), and an area under the curve of 0.980 on the data set from Sankara Nethralaya. CONCLUSIONS AND RELEVANCE: This study shows that the automated DR system generalizes to this population of Indian patients in a prospective setting and demonstrates the feasibility of using an automated DR grading system to expand screening programs.

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Authors: Gulshan, V., Rajan, R. P., Widner, K., Wu, D., Wubbels, P., Rhodes, T., Whitehouse, K., Coram, M., Corrado, G., Ramasamy, K., Raman, R., Peng, L., & Webster, D. R.
Affiliations: Google (United States) (US)
Aravind Eye Hospital (IN)
Verily Life Sciences LLC (United States) (US)
Sankara Nethralaya (IN)
Corresponding author: Peng, Lily
ORCID: Rajan, Renu P.
Wu, Derek
Coram, Marc
Corrado, Greg
Ramasamy, Kim
Raman, Rajiv
Peng, Lily
Webster, Dale R.
Venue: JAMA Ophthalmology
Publisher: American Medical Association (AMA)
Published: 2019-09-01
Volume: 137
Issue: 9
Pages: 987
ISSN: 2168-6165 (ISSN-L)
DOI: 10.1001/jamaophthalmol.2019.2004
PMC: PMC6567842
OpenAlex: W2949074827
Cited by: 303
References: 21
Citations by year: 2026: 33 · 2025: 56 · 2024: 43 · 2023: 50 · 2022: 39 · 2021: 38 · 2020: 36 · 2019: 7
FWCI: 19.80
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: JAMA Ophthalmology (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: Google
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, deep learning, retinal fundus photographs, diabetic macular edema, manual grading, sensitivity and specificity, retinal screening, diabetes mellitus, India, model validation, resource-limited settings, Aravind Eye Hospital

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Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India

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