Performance of a Deep-Learning Algorithm vs Manual Grading for Detecting Diabetic Retinopathy in India
Open accessAbstract
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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Given sufficient data, the accuracy of computer vision models often matches or surpasses the level of expert physicians.
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From Deep learning-enabled medical computer vision (2021) · cites this paper as [16]
Side-by-side deployment of ML models with existing workflows is an effective tool to build physician trust in AI results.
“Another effective tool to build physician trust in AI results is sideby-side deployment of ML models with existing workflows (e.g manual grading 16 ).”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [16]
Twenty-five ophthalmology studies compared algorithm performance against healthcare professionals.
“Twenty-five studies17,28,29,35,37,39,40,44–61 compared algorithm performance against healthcare professionals.”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [17]
Only eight ophthalmology studies used prospectively collected data.
“Only eight studies14–21 used prospectively collected data”
From Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis (2021) · cites this paper as [17]
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Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
Self-citationTHE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL
Clopper, C. J., & Pearson, E. S. (1934) · Biometrika · doi:10.1093/biomet/26.4.404 · Cited by 5,106 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
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., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0040-6 · Cited by 1,638 · see citing works
Automated Identification of Diabetic Retinopathy Using Deep Learning
Gargeya, R., & Leng, T. (2017) · Ophthalmology · doi:10.1016/j.ophtha.2017.02.008 · Cited by 1,386 · see citing works
Burlina, P., Joshi, N., Pekala, M. J., Pacheco, K. D., Freund, D., & Bressler, N. M. (2017) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2017.3782 · Cited by 685 · see citing works
The current state of diabetes mellitus in India
Kaveeshwar, S. A. (2014) · Australasian Medical Journal · doi:10.4066/amj.2014.1979 · Cited by 685 · see citing works
Krause, J., Gulshan, V., Rahimy, E., Karth, P., Widner, K., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Ophthalmology · doi:10.1016/j.ophtha.2018.01.034 · Cited by 564 · see citing works
Self-citationRema, M., Premkumar, S., Anitha, B., Deepa, R., Pradeepa, R., & Mohan, V. (2005) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.05-0019 · Cited by 523 · see citing works
Detecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506 · see citing works
Self-citationAutomated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy
Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., Massin, P., Cochener, B., Gain, P., Tang, L., et al. (2013) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2013.1743 · Cited by 474 · see citing works
The current state of diabetes mellitus in India.
Kaveeshwar, S. A., & Cornwall, J. (2014) · PubMed · doi:10.4066/amj.2013.1979 · Cited by 393 · see citing works
Diabetic retinopathy among self reported diabetics in southern India: a population based assessment
Narendran, V. (2002) · British Journal of Ophthalmology · doi:10.1136/bjo.86.9.1014 · Cited by 172 · see citing works
The Cost–Utility of Telemedicine to Screen for Diabetic Retinopathy in India
Rachapelle, S., Legood, R., Alavi, Y., Lindfield, R., Sharma, T., Kuper, H., & Polack, S. (2012) · Ophthalmology · doi:10.1016/j.ophtha.2012.09.002 · Cited by 171 · see citing works
A telemedical approach to the screening of diabetic retinopathy: digital fundus photography.
Liesenfeld, B., KOHNER, E. M., Piehlmeier, W., Kluthe, S., Aldington, S. J., Porta, M., Bek, T., Obermaier, M., Mayer, H., Mann, G., et al. (2000) · Diabetes Care · doi:10.2337/diacare.23.3.345 · Cited by 164 · see citing works
Namperumalsamy, P., Kim, R., Vignesh, T. P., Nithya, N., Royes, J., Gijo, T., Thulasiraj, R. D., & Vijayakumar, V. (2008) · Postgraduate Medical Journal · doi:10.1136/bjo.2008.147934 · Cited by 143 · see citing works
Diabetic retinopathy management guidelines
Chakrabarti, R., Harper, C. A., & Keeffe, J. E. (2012) · Expert Review of Ophthalmology · doi:10.1586/eop.12.52 · Cited by 112 · see citing works
Wang, Y. T., Tadarati, M., Wolfson, Y., Bressler, S. B., & Bressler, N. M. (2015) · JAMA Ophthalmology · doi:10.1001/jamaophthalmol.2015.5332 · Cited by 100 · see citing works
Screening Intervals for Diabetic Retinopathy and Implications for Care
Scanlon, P. H. (2017) · Current Diabetes Reports · doi:10.1007/s11892-017-0928-6 · Cited by 58 · see citing works
Rani, P. K., Raman, R., Sharma, V., Mahuli, S. V., Tarigopala, A., Sudhir, R. R., Kumaramanickavel, G., & Sharma, T. (2007) · British Journal of Ophthalmology · doi:10.1136/bjo.2007.120659 · Cited by 51 · see citing works
Self-citationCurrent State of Care for Diabetic Retinopathy in India
Ramasamy, K., Raman, R., & Tandon, M. (2013) · Current Diabetes Reports · doi:10.1007/s11892-013-0388-6 · Cited by 44 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Paper presented at the 2016 IEEE Conference on Computer Vision and Pattern Recognition; June 27-30, 2016; Las Vegas, NV. https://ieeexplore.ieee.org/document/7780677. Accessed May 12, 2019.
Cited by
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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., Raman, R., Tan, G. S. W., Schmetterer, L. F., Keane, P. A., & Wong, T. Y. (2018) · Springer eBooks · doi:10.1007/978-3-030-64573-1_200 · Cited by 1,616 · see citing works
Self-citationDeep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works
Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S. W., Karthikesalingam, A., King, D., Ashrafian, H., & Darzi, A. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00438-z · Cited by 946 · see citing works
Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective
Li, J.-P. O., Liu, H., Ting, D. S. J., Jeon, S., Chan, R. V. P., Kim, J. E., Sim, D. A., Thomas, P. B. M., Lin, H., Chen, Y., et al. (2021) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2020.100900 · Cited by 626 · see citing works
Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
Celi, L. A., Cellini, J., Charpignon, M.-L., Dee, E. C., Dernoncourt, F., Eber, R., Mitchell, W. G., Moukheiber, L., Schirmer, J., Situ, J., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000022 · Cited by 576 · see citing works
The myth of generalisability in clinical research and machine learning in health care
Futoma, J., Simons, M., Panch, T., Doshi-Velez, F., & Celi, L. A. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30186-2 · Cited by 467 · see citing works
Applications of deep learning in fundus images: A review
Li, T., Wang, B., Hu, C., Kang, H., Liu, H., Wang, K., & Fu, H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101971 · Cited by 406 · see citing works
Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm
Krishnan, G., Singh, S., Pathania, M., Gosavi, S., Abhishek, S., Parchani, A., & Dhar, M. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1227091 · Cited by 326 · see citing works
Machine learning in clinical decision making
Adlung, L., Cohen, Y., Mor, U., & Elinav, E. (2021) · Med · doi:10.1016/j.medj.2021.04.006 · Cited by 304 · see citing works
Xie, Y., Nguyen, Q. D., Hamzah, H., Lim, G., Bellemo, V., Gunasekeran, D. V., Yip, M. Y. T., Qi Lee, X., Hsu, W., Li Lee, M., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30060-1 · Cited by 288 · see citing works
Ruamviboonsuk, P., Tiwari, R., Sayres, R., Nganthavee, V., Hemarat, K., Kongprayoon, A., Raman, R., Levinstein, B., Liu, Y., Schaekermann, M., et al. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00017-6 · Cited by 250 · see citing works
Self-citationDiabetic retinopathy and diabetic macular oedema pathways and management: UK Consensus Working Group
Amoaku, W. M. K., Ghanchi, F. D., Bailey, C., Banerjee, S., Banerjee, S., Downey, L., Gale, R. P., HAMILTON, R. D., Khunti, K. K., Posner, E., et al. (2020) · Eye · doi:10.1038/s41433-020-0961-6 · Cited by 222 · see citing works
Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images
Oh, K., Kang, H. M., Leem, D., Lee, H., Seo, K. Y., & Yoon, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-81539-3 · Cited by 197 · see citing works
Artificial intelligence in ophthalmology: The path to the real-world clinic
Li, Z., Wang, L., Wu, X., Jiang, J., Qiang, W., Xie, H., Zhou, H., Wu, S., Shao, Y., & Chen, W. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101095 · Cited by 207 · see citing works
Rajesh, A. E., Davidson, O. Q., Lee, C. S., & Lee, A. (2023) · Diabetes Care · doi:10.2337/dci23-0032 · Cited by 166 · see citing works
Li, F., Wang, Y., Xu, T., Lin, D., Yan, L., Jiang, M., Zhang, X., Jiang, H., Wu, Z., & Zou, H. (2021) · Eye · doi:10.1038/s41433-021-01552-8 · Cited by 150 · see citing works
A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
Jabbar, A., Liaqat, H. B., Akram, A., Sana, M. U., Azpíroz, I. D., Díez, I. D. L. T., & Ashraf, I. (2024) · IEEE Access · doi:10.1109/access.2024.3373467 · Cited by 146 · see citing works
Vision Transformer‐based recognition of diabetic retinopathy grade
Wu, J., Hu, R., Xiao, Z., Chen, J., & Liu, J.-W. (2021) · Medical Physics · doi:10.1002/mp.15312 · Cited by 145 · see citing works
A survey on medical image analysis in diabetic retinopathy
Stolte, S. E., & Fang, R. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101742 · Cited by 133 · see citing works
Artificial intelligence for diabetic retinopathy screening, prediction and management
Gunasekeran, D. V., Ting, D. S. W., Tan, G. S. W., & Wong, T. Y. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000693 · Cited by 133 · see citing works
Olczak, J., Pavlopoulos, J., Prijs, J., IJpma, F. F. A., Doornberg, J. N., Lundström, C. F., Hedlund, J., & Gordon, M. (2021) · Acta Orthopaedica · doi:10.1080/17453674.2021.1918389 · Cited by 132 · see citing works
Usman, T. M., Saheed, Y. K., Djitog, I., & Nsang, A. S. (2023) · International Journal of Cognitive Computing in Engineering · doi:10.1016/j.ijcce.2023.02.002 · Cited by 138 · see citing works
Welcoming new guidelines for AI clinical research
Topol, E. J. (2020) · Nature Medicine · doi:10.1038/s41591-020-1042-x · Cited by 116 · see citing works
A Survey on Deep-Learning-Based Diabetic Retinopathy Classification
Sebastian, A., Elharrouss, O., Al-Maadeed, S. A., & Almaadeed, N. (2023) · Diagnostics · doi:10.3390/diagnostics13030345 · Cited by 113 · see citing works
Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network
Pao, S., Lin, H.-Z., Chien, K., Tai, M., Chen, J., & Lin, G. (2020) · Journal of Ophthalmology · doi:10.1155/2020/9139713 · Cited by 111 · see citing works
Zhang, Y., Shi, J., Peng, Y. H., Zhao, Z., Zheng, Q., Wang, Z., Liu, K., Jiao, S., Qiu, K., Zhou, Z., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001596 · Cited by 111 · see citing works
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Jabbar, M. K., Yan, J., Xu, H., Rehman, Z. U., & Jabbar, A. (2022) · Brain Sciences · doi:10.3390/brainsci12050535 · Cited by 105 · see citing works
González-Gonzalo, C., Thee, E. F., Klaver, C. C. W., Lee, A. Y., Schlingemann, R. O., Tufail, A., Verbraak, F., & Sánchez, C. I. (2022) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2021.101034 · Cited by 103 · see citing works
Nadeem, M. W., Goh, H. G., Hussain, M., Liew, S., Andonović, I., & Khan, M. A. (2022) · Sensors · doi:10.3390/s22186780 · Cited by 115 · see citing works
Wu, Z., Zhang, C., Gu, X., Duporge, I., Hughey, L. F., Stabach, J. A., Skidmore, A. K., Hopcraft, J. G. C., Lee, S. J., Atkinson, P. M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38901-y · Cited by 100 · see citing works
Ting, D. S. W., Lin, H., Ruamviboonsuk, P., Wong, T. Y., & Sim, D. A. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30217-1 · Cited by 98 · see citing works
Lim, G., Bellemo, V., Xie, Y., Lee, X. Q., Yip, M. Y. T., & Ting, D. S. W. (2020) · Eye and Vision · doi:10.1186/s40662-020-00182-7 · Cited by 96 · see citing works
Selvachandran, G., Quek, S. G., Paramesran, R., Ding, W., & Sơn, L. H. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10185-6 · Cited by 96 · see citing works
Human-AI Collaboration: The Effect of AI Delegation on Human Task Performance and Task Satisfaction
Hemmer, P., Westphal, M., Schemmer, M., Vetter, S., Vössing, M., & Satzger, G. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584052 · Cited by 111 · see citing works
Wu, J.-H., Liu, T. Y. A., Hsu, W.-T., Ho, J. H.-C., & Lee, C.-C. (2021) · Journal of Medical Internet Research · doi:10.2196/23863 · Cited by 108 · see citing works
Sosale, B., Aravind, S. R., Murthy, H., Narayana, S., Sharma, U., GOWDA, S. G. V., & Naveenam, M. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000892 · Cited by 85 · see citing works
Ikram, A., & Imran, A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109656 · Cited by 83 · see citing works
Artificial intelligence in retinal disease: clinical application, challenges, and future directions
Varela, M. D., Sen, S., de Guimarães, T. A. C., Kabiri, N., Pontikos, N., Balaskas, K., & Michaelides, M. (2023) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-023-06052-x · Cited by 86 · see citing works
A Survey on Multi-modal Summarization
Jangra, A., Mukherjee, S., Jatowt, A., Saha, S., & Hasanuzzaman, M. (2023) · ACM Computing Surveys · doi:10.1145/3584700 · Cited by 80 · see citing works
JOSEPH, S., SELVARAJ, J., MANI, I., KUMARAGURUPARI, T., SHANG, X., MUDGIL, P., RAVILLA, T., & HE, M. (2024) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2024.02.012 · Cited by 69 · see citing works
Sosale, B., Sosale, A., Murthy, H., Sengupta, S., & Naveenam, M. (2020) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1203_19 · Cited by 69 · see citing works
Bhimavarapu, U., & Battineni, G. (2022) · Healthcare · doi:10.3390/healthcare11010097 · Cited by 73 · see citing works
A Review of the Machine Learning Algorithms for Covid-19 Case Analysis
Tiwari, S., Chanak, P., & Singh, S. K. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3142241 · Cited by 66 · see citing works
Cleland, C. R., Rwiza, J., Evans, J. R., Gordon, I., Macleod, D., Burton, M. J., & Bascarán, C. (2023) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2023-003424 · Cited by 64 · see citing works
Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs
Liu, X., Ali, T. K., Singh, P., Shah, A., McKinney, S. M., Ruamviboonsuk, P., Turner, A. W., Keane, P. A., Chotcomwongse, P., Nganthavee, V., et al. (2022) · Ophthalmology Retina · doi:10.1016/j.oret.2021.12.021 · Cited by 62 · see citing works
Self-citationLi, Z., Guo, C., Nie, D., Lin, D., Zhu, Y., Chen, C., Zhang, L., Xu, F., Jin, C., Zhang, X., et al. (2019) · Annals of Translational Medicine · doi:10.21037/atm.2019.11.28 · Cited by 58 · see citing works
Huemer, J. C., Wagner, S. K., & Sim, D. A. (2020) · Clinical ophthalmology · doi:10.2147/opth.s261629 · Cited by 62 · see citing works
Gunasekeran, D. V., Zheng, F., Lim, G. Y. S., Chong, C. C. Y., Zhang, S., Ng, W. Y., Keel, S., Xiang, Y., Park, K. H., Park, S. J., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.875242 · Cited by 60 · see citing works
Self-citationXie, Y., Gunasekeran, D. V., Balaskas, K., Keane, P. A., Sim, D. A., Bachmann, L. M., Macrae, C., & Ting, D. S. W. (2020) · Translational Vision Science & Technology · doi:10.1167/tvst.9.2.22 · Cited by 59 · see citing works
Big data, machine learning and artificial intelligence: a neurologist’s guide
Auger, S. D., Jacobs, B. M., Dobson, R., Marshall, C. R., & Noyce, A. J. (2020) · Practical Neurology · doi:10.1136/practneurol-2020-002688 · Cited by 53 · see citing works
Hybrid Graph Convolutional Network for Semi-Supervised Retinal Image Classification
Zhang, G., Pan, J. S., Zhang, Z., Zhang, H., Xing, C., Sun, B., & Li, M. (2021) · IEEE Access · doi:10.1109/access.2021.3061690 · Cited by 48 · see citing works
Artificial intelligence for diabetic retinopathy
Li, S., Zhao, R., & Zou, H. (2021) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001816 · Cited by 53 · see citing works
Gunasekaran, K., Pitchai, R., Chaitanya, G. K., Selvaraj, D., Sheryl, S. A., Almoallim, H. S. M., Alharbi, S. A., Raghavan, S. S., & Tesemma, B. G. (2022) · BioMed Research International · doi:10.1155/2022/3163496 · Cited by 51 · see citing works
RetractedOlvera‐Barrios, A., Heeren, T. F. C., Balaskas, K., Chambers, R., Bolter, L., Egan, C. A., Tufail, A., & Anderson, J. (2020) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2019-315394 · Cited by 49 · see citing works
Artificial Intelligence to Reduce Ocular Health Disparities: Moving From Concept to Implementation
Campbell, J. P., Mathenge, C., Cherwek, H., Balaskas, K., Pasquale, L. R., Keane, P. A., & Chiang, M. F. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.10.3.19 · Cited by 48 · see citing works
Gao, Z., Jin, K., Yan, Y., Liu, X., Shi, Y., Ge, Y., Pan, X., Lu, Y., Wu, J., Wang, Y., et al. (2022) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-021-05503-7 · Cited by 48 · see citing works
Resilience to diabetic retinopathy
Serikbaeva, A., Li, Y., Ma, S., Yi, D., & Kazlauskas, A. (2024) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2024.101271 · Cited by 45 · see citing works
Optimizing human-centered AI for healthcare in the Global South
Okolo, C. T. (2022) · Patterns · doi:10.1016/j.patter.2021.100421 · Cited by 48 · see citing works
Zhang, G., Sun, B., Chen, Z., Gao, Y., Zhang, Z., Li, K., & Yang, W. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.872214 · Cited by 43 · see citing works
MSHF: A Multi-Source Heterogeneous Fundus (MSHF) Dataset for Image Quality Assessment
Jin, K., Gao, Z., Jiang, X., Wang, Y., Ma, X., Li, Y., & Ye, J. (2023) · Scientific Data · doi:10.1038/s41597-023-02188-x · Cited by 42 · see citing works
Enhancing Ophthalmic Diagnosis and Treatment with Artificial Intelligence
Olawade, D. B., Weerasinghe, K., Mathugamage, M. D. D. E., Odetayo, A., Aderinto, N., Teke, J., & Boussios, S. (2025) · Medicina · doi:10.3390/medicina61030433 · Cited by 50 · see citing works
Xu, L., Yang, C., Zhang, F., Cheng, X., Wei, Y., Fan, S., Liu, M., He, X., Deng, J., Xie, T., et al. (2022) · Cancers · doi:10.3390/cancers14112574 · Cited by 41 · see citing works
Wintergerst, M. W. M., Bejan, V., Hartmann, V., Schnorrenberg, M., Bleckwenn, M., Weckbecker, K., & Finger, R. P. (2022) · Ophthalmic Epidemiology · doi:10.1080/09286586.2021.1939886 · Cited by 39 · see citing works
Zhang, S., Li, Y., Liu, W., Qi, C., Wang, S., Li, J., & Chen, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1630408 · Cited by 40 · see citing works
Barrero‐Castillero, A., Corwin, B. K., VanderVeen, D. K., & Wang, J. C. (2020) · Pediatric Clinics of North America · doi:10.1016/j.pcl.2020.04.012 · Cited by 39 · see citing works
Using artificial intelligence for diabetic retinopathy screening: Policy implications
Raman, R., DasGupta, D., Ramasamy, K., George, R., Mohan, V., & Ting, D. (2021) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1420_21 · Cited by 29 · see citing works
Self-citationLeveraging physiology and artificial intelligence to deliver advancements in health care
Zhang, A., Wu, Z., Wu, E., Wu, M. A., Snyder, M. P., Zou, J., & Wu, J. C. (2023) · Physiological Reviews · doi:10.1152/physrev.00033.2022 · Cited by 41 · see citing works
Bias and Non-Diversity of Big Data in Artificial Intelligence: Focus on Retinal Diseases
Jacoba, C. M. P., Celi, L. A., Lorch, A., Fickweiler, W., Sobrin, L., Gichoya, J. W., Aiello, L. P., & Silva, P. S. (2023) · Seminars in Ophthalmology · doi:10.1080/08820538.2023.2168486 · Cited by 38 · see citing works
Bhimavarapu, U., Chintalapudi, N., & Battineni, G. (2023) · Diagnostics · doi:10.3390/diagnostics13152606 · Cited by 36 · see citing works
Lara, M. A. R., Kowalczuk, M. V. R., Eliceche, M. L., Ferraresso, M. G., Luna, D. R., Benítez, S., & Mazzuoccolo, L. D. (2023) · Scientific Data · doi:10.1038/s41597-023-02630-0 · Cited by 40 · see citing works
Chai, Y., Liu, H., Xu, J., Samtani, S., Jiang, Y., & Liu, H. (2022) · ACM Transactions on Management Information Systems · doi:10.1145/3561653 · Cited by 33 · see citing works
Accelerating ophthalmic artificial intelligence research: the role of an open access data repository
Kras, A., Celi, L. A., & Miller, J. B. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000678 · Cited by 33 · see citing works
Li, N., Ma, M., Lai, M., Gu, L., Kang, M., Wang, Z., Jiao, S., Dang, K., Deng, J., Ding, X., et al. (2021) · Journal of Diabetes · doi:10.1111/1753-0407.13241 · Cited by 31 · see citing works
Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00889-6 · Cited by 33 · see citing works
Nunez do Rio, J. M., Nderitu, P., Raman, R., Rajalakshmi, R., Kim, R., Rani, P. K., Sivaprasad, S., Bergeles, C., for the SMART India Study Group, Raman, R., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-28347-z · Cited by 31 · see citing works
Self-citationAutomation of Systematic Reviews with Large Language Models
Cao, C., Arora, R., Cento, P., Budak, A., Manta, K., Farahani, E., Cecere, M., Selemon, A., Sang, J., Gong, L. X., et al. (2025) · medRxiv · doi:10.1101/2025.06.13.25329541 · Cited by 33 · see citing works
The application of artificial intelligence in diabetic retinopathy: progress and prospects
Xu, X., Zhang, M., Huang, S., Li, X., Kui, X., & Liu, J. (2024) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2024.1473176 · Cited by 30 · see citing works
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting (Print) · doi:10.1080/1061186x.2024.2448711 · Cited by 18 · see citing works
Trimpl, M. J., Primakov, S. P., Lambin, P., Stride, E., Vallis, K. A., & Gooding, M. J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac6d9c · Cited by 30 · see citing works
Progress of artificial intelligence in diabetic retinopathy screening
Wang, Y., Yang, J., Yang, J., Zhao, X., Chen, Y., & Yu, W. (2020) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3414 · Cited by 29 · see citing works
Generalisability through local validation: overcoming barriers due to data disparity in healthcare
Mitchell, W. G., Dee, E. C., & Celi, L. A. (2021) · BMC Ophthalmology · doi:10.1186/s12886-021-01992-6 · Cited by 29 · see citing works
Towards better healthcare: What could and should be automated?
Fruehwirt, W., & Duckworth, P. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120967 · Cited by 31 · see citing works
Alqahtani, A. S., Alshareef, W. M., Aljadani, H. T., Hawsawi, W. O., & Shaheen, M. H. (2025) · International Journal of Retina and Vitreous · doi:10.1186/s40942-025-00670-9 · Cited by 37 · see citing works
Artificial Intelligence in Ophthalmology: Evolutions in Asia
Ruamviboonsuk, P., Cheung, C. Y.-L., Zhang, X., Raman, R., Park, S. J., & Ting, D. S. W. (2020) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/01.apo.0000656980.41190.bf · Cited by 28 · see citing works
Self-citationArtificial intelligence in ophthalmology and healthcare
Bali, J., & Bali, O. (2020) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1848_19 · Cited by 28 · see citing works
Zhelev, Z., Peters, J., Rogers, M., Allen, M. J., Kijauskaite, G., Seedat, F., Wilkinson, E. C., & Hyde, C. (2023) · Journal of Medical Screening · doi:10.1177/09691413221144382 · Cited by 30 · see citing works
Katada, Y., Ozawa, N., Masayoshi, K., Ofuji, Y., Tsubota, K., & Kurihara, T. (2020) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2020.100024 · Cited by 27 · see citing works
Telemedicine in diabetic retinopathy screening in India
Ramasamy, K., Mishra, C., Kannan, N. B., Namperumalsamy, P., & Sen, S. (2021) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1442_21 · Cited by 27 · see citing works
Self-citationNoriega-Campero, A., Meizner, D., Camacho‐García‐Formentí, D., Enciso, J., Quiroz-Mercado, H., Morales-Cantón, V., Almaatouq, A., & Pentland, A. (2021) · JMIR Formative Research · doi:10.2196/25290 · Cited by 23 · see citing works
Wang, Z., Li, Z., Li, K., Mu, S., Zhou, X., & Di, Y. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1197783 · Cited by 27 · see citing works
Application of Artificial Intelligence in Targeting Retinal Diseases
Sorrentino, F. S., Jurman, G., Nadai, K. D., Campa, C., Furlanello, C., & Parmeggiani, F. (2020) · Current Drug Targets · doi:10.2174/1389450121666200708120646 · Cited by 25 · see citing works
The Validation of Deep Learning-Based Grading Model for Diabetic Retinopathy
Zhang, W.-F., Li, D., Wei, Q., Ding, D., Meng, L., Wang, Y., Zhao, X., & Chen, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.839088 · Cited by 25 · see citing works
Duggal, M., Chauhan, A., Gupta, V., Kankaria, A., Budhija, D., Verma, P., Miglani, V., Syal, P., Kaur, G., Kumar, L., et al. (2025) · JMIR Medical Informatics · doi:10.2196/67529 · Cited by 24 · see citing works
Uy, H., Fielding, C., Hohlfeld, A., Ochodo, E., Opare, A., Mukonda, E. E., Minnies, D., & Engel, M. E. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0002160 · Cited by 30 · see citing works
Performance of a Deep Learning Diabetic Retinopathy Algorithm in India
Brant, A. R., Singh, P., Yin, X., Yang, L., Nayar, J., Jeji, D., Matias, Y., Corrado, G. S., Webster, D. R., Virmani, S., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.0984 · Cited by 23 · see citing works
Self-citationChu, A., Squirrell, D., Phillips, A. M., & Vaghefi, E. (2020) · Journal of Ophthalmology · doi:10.1155/2020/8841927 · Cited by 24 · see citing works
do Rio, J. M. N., Nderitu, P., Bergeles, C., Sivaprasad, S., Tan, G. S. W., & Raman, R. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11030614 · Cited by 23 · see citing works
Self-citationRevolutionizing Diabetic Retinopathy Screening: Integrating AI-Based Retinal Imaging in Primary Care
Beals, D., Simon, L., Rogers, F., & Pogroszewski, S. (2025) · Journal of CME · doi:10.1080/28338073.2024.2437294 · Cited by 26 · see citing works
Kaur, J., & Kaur, P. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7138 · Cited by 22 · see citing works
- 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
- Venue
- JAMA Ophthalmology
- Publisher
- American Medical Association (AMA)
- Published
- 2019-09-01
- Volume
- 137
- Issue
- 9
- Pages
- 987
- ISSN
- 2168-6165 (ISSN-L)
- 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 · 2018: 1
- FWCI
- 19.80
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA Ophthalmology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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