Results of Automated Retinal Image Analysis for Detection of Diabetic Retinopathy from the Nakuru Study, Kenya
Open accessAbstract
OBJECTIVE: Digital retinal imaging is an established method of screening for diabetic retinopathy (DR). It has been established that currently about 1% of the world's blind or visually impaired is due to DR. However, the increasing prevalence of diabetes mellitus and DR is creating an increased workload on those with expertise in grading retinal images. Safe and reliable automated analysis of retinal images may support screening services worldwide. This study aimed to compare the Iowa Detection Program (IDP) ability to detect diabetic eye diseases (DED) to human grading carried out at Moorfields Reading Centre on the population of Nakuru Study from Kenya. PARTICIPANTS: Retinal images were taken from participants of the Nakuru Eye Disease Study in Kenya in 2007/08 (n = 4,381 participants [NW6 Topcon Digital Retinal Camera]). METHODS: First, human grading was performed for the presence or absence of DR, and for those with DR this was sub-divided in to referable or non-referable DR. The automated IDP software was deployed to identify those with DR and also to categorize the severity of DR. MAIN OUTCOME MEASURES: The primary outcomes were sensitivity, specificity, and positive and negative predictive value of IDP versus the human grader as reference standard. RESULTS: Altogether 3,460 participants were included. 113 had DED, giving a prevalence of 3.3% (95% CI, 2.7-3.9%). Sensitivity of the IDP to detect DED as by the human grading was 91.0% (95% CI, 88.0-93.4%). The IDP ability to detect DED gave an AUC of 0.878 (95% CI 0.850-0.905). It showed a negative predictive value of 98%. The IDP missed no vision threatening retinopathy in any patients and none of the false negative cases met criteria for treatment. CONCLUSIONS: In this epidemiological sample, the IDP's grading was comparable to that of human graders'. It therefore might be feasible to consider inclusion into usual epidemiological grading.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The International Clinical Diabetic Retinopathy severity scale was the most commonly utilized severity scale, used in 15 out of 30 studies.
“Various severity scales are used to classify diabetic retinopathy (DR), with the International Clinical Diabetic Retinopathy (ICDR) severity scale being the most commonly utilized in 15 out of 30 studies [9, 11–13, 15, 18, 21–23, 32, 34–36].”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [15]
The Methods section (“The grading protocol and ICDR reference standard”; page unavailable) and Table 1 show that this study used ICDR severity levels, so it supports counting this paper as one example. It does not assess 30 studies or establish that ICDR was the most commonly used scale in 15 of them. (LLM, conf=0.86)
Experimental — accuracy not yet measuredImage acquisition was often performed by trained non-physician staff, while final grading was typically carried out by ophthalmologists or retinal specialists in 22 out of 30 studies.
“Image acquisition was often performed by trained non-physician staff (technicians, optometrists, nurses, paramedics), while final grading was typically carried out by ophthalmologists or retinal specialists (22/30 studies) [8–13, 15, 16, 18, 21, 22, 24, 26, 27, 30–37]”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [15]
Methods > Subject recruitment and imaging reports retinal photography and says that human graders at Moorfields Eye Hospitals Reading Centre graded the photographs. It does not identify who acquired the images or establish that its graders were ophthalmologists or retinal specialists. As a single-study report, it also does not provide the cited sentence’s 22/30-studies figure. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRemote grading of retinal images was widely utilized, with 18 out of 30 studies relying on this approach.
“Remote grading of retinal images is widely utilized, with 18 out of 30 studies relying on this approach [10, 15, 16, 18, 21–27, 29–35, 37]”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [15]
The predominant method for managing remotely graded images was store and forward (14 out of 30 studies), with seven studies adopting real-time processing.
“The predominant method for managing these remotely graded images is ‘store and forward’ (14 out of 30 studies) [15, 16, 18, 22–24, 26–30, 33, 35, 37], with seven studies adopting the ‘real-time’ processing method [8, 10, 11, 25, 31, 32, 34]”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [15]
Studies using AI software such as Iowa Diabetic Retinopathy detection software and EyeArt show high sensitivity up to 95.8% (95% CI 92.9-98.7) and diagnostic accuracy.
“Studies using AI software, such as the Iowa Diabetic Retinopathy detection software [15] and EyeArt [21] show high sensitivity up to 95,8% (95% CI 92,9–98,7) and diagnostic accuracy [16].”
From Diabetic retinopathy screening model in low and middle-income countries: a scoping review (2025) · cites this paper as [15]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Global estimates of the prevalence of diabetes for 2010 and 2030
Shaw, J. E., Sicree, R. A., & Zimmet, P. Z. (2010) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2009.10.007 · Cited by 7,216 · see citing works
Lang, G. E., Vaiser, A., Snyder, W. B., & Lynn, J. R. (1974) · doi:10.1159/isbn.978-3-318-01439-6 · Cited by 5,583 · see citing works
Wilkinson, C. P., Ferris, F. L., Klein, R., Lee, P. P., Agardh, C., Davis, M., Dills, D. G., Kampik, A., Pararajasegaram, R., & Verdaguer, J. (2003) · Ophthalmology · doi:10.1016/s0161-6420(03)00475-5 · Cited by 3,786 · see citing works
Global estimates of visual impairment: 2010
Pascolini, D., & Mariotti, S. P. (2011) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2011-300539 · Cited by 3,773 · see citing works
Cheung, N., Mitchell, P., & Wong, T. Y. (2010) · The Lancet · doi:10.1016/s0140-6736(09)62124-3 · Cited by 3,288 · see citing works
Fundus Photographic Risk Factors for Progression of Diabetic Retinopathy
* (1991) · Ophthalmology · doi:10.1016/s0161-6420(13)38014-2 · Cited by 961 · see citing works
Liew, G., Michaelides, M., & Bunce, C. (2014) · BMJ Open · doi:10.1136/bmjopen-2013-004015 · Cited by 663 · see citing works
Global Prevalence of Vision Impairment and Blindness
Stevens, G. A., White, R. A., Flaxman, S. R., Price, H., Jonas, J. B., Keeffe, J. E., Leasher, J. L., Naidoo, K. S., Pesudovs, K., Resnikoff, S., et al. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2013.05.025 · Cited by 539 · see citing works
Automated 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
Self-citationCurrent Epidemiology of Diabetic Retinopathy and Diabetic Macular Edema
Ding, J., & Wong, T. Y. (2012) · Current Diabetes Reports · doi:10.1007/s11892-012-0283-6 · Cited by 445 · see citing works
Resnikoff, S., Felch, W., Gauthier, T.-M., & Spivey, B. (2012) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2011-301378 · Cited by 405 · see citing works
Automated Early Detection of Diabetic Retinopathy
Abràmoff, M. D., Reinhardt, J. M., Russell, S. R., Folk, J. C., Mahajan, V. B., Niemeijer, M., & Quellec, G. (2010) · Ophthalmology · doi:10.1016/j.ophtha.2010.03.046 · Cited by 287 · see citing works
Self-citationAbràmoff, M. D., Niemeijer, M., Suttorp-Schulten, M. S. A., Viergever, M. A., Russell, S. R., & van Ginneken, B. (2008) · Diabetes Care · doi:10.2337/dc07-1312 · Cited by 277 · see citing works
Self-citationPalmer, J. J., Chinanayi, F., Gilbert, A., Pillay, D., Fox, S., Jaggernath, J., Naidoo, K. S., Graham, R., Patel, D., & Blanchet, K. (2014) · Human Resources for Health · doi:10.1186/1478-4491-12-44 · Cited by 155 · see citing works
Mathenge, W., Bastawrous, A., Peto, T., Leung, I., Yorston, D., Foster, A., & Kuper, H. (2014) · Ophthalmic Epidemiology · doi:10.3109/09286586.2014.903982 · Cited by 51 · see citing works
Self-citationThe global inverse care law: a distorted map of blindness
Bastawrous, A., & Hennig, B. D. (2012) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2012-302088 · Cited by 49 · see citing works
Self-citationThe Nakuru eye disease cohort study: methodology & rationale
Bastawrous, A., Mathenge, W. C., Pető, T., Weiss, H. A., Rono, H. K., Foster, A., Burton, M. J., & Kuper, H. (2014) · BMC Ophthalmology · doi:10.1186/1471-2415-14-60 · Cited by 27 · see citing works
Self-citation
Flagged first, then by citations
1 of the 18 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
Self-citationArtificial 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
Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.16-19964 · Cited by 1,101 · see citing works
Self-citationDiabetes in sub-Saharan Africa: from clinical care to health policy
Atun, R. A., Davies, J. I., Gale, E. A., Bärnighausen, T., Beran, D., Kengne, A. P., Levitt, N. S., Mangugu, F. W., Nyirenda, M., Ogle, G. D., et al. (2017) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(17)30181-x · Cited by 558 · see citing works
Bellemo, V., Lim, Z. W., Lim, G., Nguyen, Q. D., Xie, Y., Yip, M. Y. T., Hamzah, H., Ho, J., Lee, X. Q., Hsu, W., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30004-4 · Cited by 419 · see citing works
Artificial intelligence for diabetic retinopathy screening: a review
Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., & Ting, D. S. W. (2020) · Eye · doi:10.1038/s41433-019-0566-0 · Cited by 376 · see citing works
Self-citationvan der Heijden, A. A., Abramoff, M. D., Verbraak, F., van Hecke, M. V., Liem, A., & Nijpels, G. (2018) · Acta Ophthalmologica · doi:10.1111/aos.13613 · Cited by 296 · see citing works
Self-citationArtificial Intelligence in Medicine: Where Are We Now?
Kulkarni, S., Seneviratne, N., Baig, M. S., & Khan, A. H. A. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.001 · Cited by 289 · see citing works
Xu, K., Feng, D., & Mi, H. (2017) · Molecules · doi:10.3390/molecules22122054 · Cited by 282 · see citing works
Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application
Bellemo, V., Lim, G., Rim, T. H., Tan, G. S. W., Cheung, C. Y.-L., Sadda, S. R., He, M., Tufail, A., Lee, M. L., Hsu, W., et al. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1189-3 · Cited by 177 · see citing works
Fundus images analysis using deep features for detection of exudates, hemorrhages and microaneurysms
Khojasteh, P., Aliahmad, B., & Kumar, D. (2018) · BMC Ophthalmology · doi:10.1186/s12886-018-0954-4 · Cited by 132 · see citing works
Verbraak, F. D., Abramoff, M. D., Bausch, G. C. F., Klaver, C., Nijpels, G., Schlingemann, R. O., & van der Heijden, A. A. (2019) · Diabetes Care · doi:10.2337/dc18-0148 · Cited by 115 · see citing works
Oculomics: Current concepts and evidence
Zhu, Z., Wang, Y., Qi, Z., Hu, W., Zhang, X., Wagner, S. K., Wang, Y., Ran, A. R., Ong, J., Waisberg, E., et al. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101350 · Cited by 94 · see citing works
Grzybowski, A. E., Singhanetr, P., Nanegrungsunk, O., & Ruamviboonsuk, P. (2023) · Ophthalmology and Therapy · doi:10.1007/s40123-023-00691-3 · Cited by 86 · 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
Abramoff, M. D., Whitestone, N., Patnaik, J. L., Rich, E., Ahmed, M., Husain, L., Hassan, M. Y., Tanjil, M. S. H., Weitzman, D., Dai, T., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00931-7 · Cited by 81 · see citing works
Self-citationMathenge, W., Whitestone, N., Nkurikiye, J., Patnaik, J. L., Piyasena, P., Uwaliraye, P., Lanouette, G., Kahook, M. Y., Cherwek, D. H., Congdon, N., et al. (2022) · Ophthalmology Science · doi:10.1016/j.xops.2022.100168 · Cited by 80 · see citing works
Self-citationA review on automatic analysis techniques for color fundus photographs
Besenczi, R., Tóth, J. A., & Hajdú, A. (2016) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.10.001 · Cited by 73 · see citing works
Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network
Hemanth, D. J., Anitha, J., Sơn, L. H., & Mittal, M. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1111-6 · Cited by 71 · 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
Artificial intelligence for diabetic retinopathy screening in Africa
Mathenge, W. C. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30009-3 · Cited by 63 · see citing works
Self-citationAutomated Screening for Diabetic Retinopathy – A Systematic Review
Nørgaard, M. F., & Grauslund, J. (2018) · Ophthalmic Research · doi:10.1159/000486284 · Cited by 63 · see citing works
Wolf, R. M., Liu, T. Y. A., Thomas, C., Prichett, L., Zimmer-Galler, I., Smith, K., Abramoff, M. D., & Channa, R. (2021) · Diabetes Care · doi:10.2337/dc20-1671 · Cited by 62 · see citing works
Self-citationHuemer, J. C., Wagner, S. K., & Sim, D. A. (2020) · Clinical ophthalmology · doi:10.2147/opth.s261629 · Cited by 62 · see citing works
DeBuc, D. C. (2016) · Current Diabetes Reports · doi:10.1007/s11892-016-0827-2 · Cited by 55 · see citing works
Bawankar, P., Shanbhag, N. U., S, S. K., Dhawan, B., Palsule, A. C., Kumar, D., Chandel, S., & Sood, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189854 · Cited by 55 · 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
Retinal biomarkers provide “insight” into cortical pharmacology and disease
Nguyen, C. T. O., Hui, F., Charng, J., Velaedan, S., van Koeverden, A. K., Lim, J. K. H., He, Z., Wong, V. H. Y., Vingrys, A. J., Bui, B. V., et al. (2017) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2017.02.009 · Cited by 45 · see citing works
Diabetic Eye Screening: Knowledge and Perspectives from Providers and Patients
Liu, Y., & Swearingen, R. (2017) · Current Diabetes Reports · doi:10.1007/s11892-017-0911-2 · Cited by 42 · see citing works
Muthusamy, D., & Palani, P. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107035 · Cited by 41 · see citing works
Artificial intelligence in ophthalmology
Patoni, S. I. P., Muşat, A. A. M., Patoni, C., Popescu, M.-N., Munteanu, M., Costache, I. B., Pîrvulescu, R. A., & Mușat, O. (2023) · Romanian Journal of Ophthalmology · doi:10.22336/rjo.2023.37 · Cited by 38 · see citing works
Arenas-Cavalli, J. T., Abarca, I., Rojas-Contreras, M., Bernuy, F., & Donoso, R. (2021) · Eye · doi:10.1038/s41433-020-01366-0 · Cited by 38 · see citing works
Wang, X., Dai, L., Li, S., Kong, H., Sheng, B., & Wu, Q. E. (2020) · Current Eye Research · doi:10.1080/02713683.2020.1764975 · Cited by 36 · see citing works
Detection of Microaneurysms in Fundus Images Based on an Attention Mechanism
Zhang, L., Feng, S., Duan, G., Li, Y., & Liu, G. (2019) · Genes · doi:10.3390/genes10100817 · Cited by 35 · see citing works
Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda
Whitestone, N., Nkurikiye, J., Patnaik, J. L., Jaccard, N., Lanouette, G., Cherwek, D. H., Congdon, N., & Mathenge, W. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-322683 · Cited by 31 · see citing works
Self-citationImpact and Trends in Global Ophthalmology
Williams, L. B., Prakalapakorn, S. G., Ansari, Z., & Goldhardt, R. (2020) · Current Ophthalmology Reports · doi:10.1007/s40135-020-00245-x · Cited by 31 · 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
Khojasteh, P., Aliahmad, B., Arjunan, S. P., & Kumar, D. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513606 · Cited by 27 · see citing works
Pei, X., & Li, Z. (2025) · BMJ Public Health · doi:10.1136/bmjph-2024-001353 · Cited by 25 · see citing works
A lightweight method for precise small lesion detection in diabetic retinopathy
Han, Z., Ye, Z., Liang, R., Dai, W., Xiao, H., Wang, X., Wu, W., Hong, C., Zheng, Q., Deng, S., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108006 · Cited by 25 · see citing works
Artificial Intelligence in Ophthalmology – Status Quo and Future Perspectives
Matos, P. A. W., Reimer, R. P., Rokohl, A. C., Caldeira, L., Heindl, L. M., & Hokamp, N. G. (2022) · Seminars in Ophthalmology · doi:10.1080/08820538.2022.2139625 · Cited by 24 · see citing works
Tahir, H. N., Ullah, N., Tahir, M., Domnic, I. S., Prabhakar, R., Meerasa, S. S., AbdElneam, A. I., Tahir, S., & Ali, Y. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1519768 · Cited by 23 · see citing works
Smartphone Assisted Fundus Fundoscopy/Photography
Barikian, A., & Haddock, L. J. (2018) · Current Ophthalmology Reports · doi:10.1007/s40135-018-0162-7 · Cited by 22 · see citing works
White, T., Selvarajah, V., Sand, F. W., Svangård, N., Mohankumar, G., Fenici, P., Rough, K., Onyango, N. O., Lyons, K., Mack, C. D., et al. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15587 · Cited by 19 · see citing works
Diabetes in sub-Saharan Africa: from clinical care to health policy
Atun, R. A., Davies, J. I., Gale, E. A. M., Bärnighausen, T., Beran, D., & Kengne, A. P. (2018) · Yearbook of pediatric endocrinology · doi:10.1530/ey.15.13.10 · Cited by 17 · see citing works
Bascarán, C., Mwangi, N., D’Esposito, F., Gordon, I., Ulloa, J. A. L., Mdala, S. Y., Ramke, J., Evans, J. R., & Burton, M. J. (2021) · Systematic Reviews · doi:10.1186/s13643-020-01553-w · Cited by 14 · see citing works
Mitigation of AI adoption bias through an improved autonomous AI system for diabetic retinal disease
Abràmoff, M. D., Lavin, P. T., Jakubowski, J. R., Blodi, B. A., Keeys, M., Joyce, C., & Folk, J. C. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01389-x · Cited by 13 · see citing works
Self-citationDiabetic retinopathy screening model in low and middle-income countries: a scoping review
Lestari, Y. D., Sitompul, R., Widyahening, I. S., Tahapary, D. L., Yusuf, P. A., Sasongko, M. B., Riono, P., Nursakina, Y., & Alexandra, G. (2025) · BMC Public Health · doi:10.1186/s12889-025-25543-6 · Cited by 11 · see citing works
Chikwelu, L., Olowoyo, J. O., Malevu, T. D., Mugivhisa, L. L., Hungwe, T., Ogunrombi, M. O., & Mkolo, N. M. (2024) · African Journal of Laboratory Medicine · doi:10.4102/ajlm.v13i1.2516 · Cited by 10 · see citing works
Morjaria, P., Bergson, S., Bastawrous, A., Watts, E., Pant, S., Gudwin, E., Zinn, M., & Chan, V. F. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000470 · Cited by 10 · see citing works
Self-citationTsai, L., Liao, K., Jang, Y., Hu, F., & Wu, W. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162383 · Cited by 10 · see citing works
Innovative Approaches in the Delivery of Primary and Secondary Eye Care
(2018) · Essentials in ophthalmology/Essentials ophthalmology · doi:10.1007/978-3-319-98014-0 · Cited by 8 · see citing works
Omar, M. O., Ali, M. J. A., Qabillie, S. E., Haji, A. I., Takriti, M. B. T., Atif, A. H., & Rangraze, I. R. (2024) · Siriraj Medical Journal/San Sirirat · doi:10.33192/smj.v76i2.266303 · Cited by 7 · see citing works
Automated detection of Diabetic Retinopathy in Three European Populations
Hansen, M. B., & Tang, H. L. (2016) · Journal of Clinical & Experimental Ophthalmology · doi:10.4172/2155-9570.1000582 · Cited by 7 · see citing works
Self-citationTowards a Device Agnostic AI for Diabetic Retinopathy Screening: An External Validation Study
Rao, D. P., Sindal, M. D., Sengupta, S., Baskaran, P., Venkatesh, R., Sivaraman, A., & Savoy, F. M. (2022) · Clinical ophthalmology · doi:10.2147/opth.s369675 · Cited by 7 · see citing works
Melander, D., Sivarasu, S., Yekinni, I. O., Yunzhang, C., & Erdman, A. G. (2020) · Journal of Medical Devices · doi:10.1115/1.4046046 · Cited by 6 · see citing works
Rigato, M., Nollino, L., Tiago, A., Spedicato, L., Simango, L. M. C., Putoto, G., Avogaro, A., & Fadini, G. P. (2022) · Acta Diabetologica · doi:10.1007/s00592-021-01834-3 · Cited by 6 · see citing works
Artificial Intelligence for the Diagnosis and Screening of Retinal Diseases
Arrigo, A., Aragona, E., & Bandello, F. M. (2023) · touchREVIEWS in Ophthalmology · doi:10.17925/usor.2023.17.2.1 · Cited by 6 · see citing works
Artificial Intelligence in Medicine: Diabetes as a Model
Sridhar, G. R., & Lakshmi, G. (2021) · Studies in big data · doi:10.1007/978-981-16-0415-7_14 · Cited by 6 · see citing works
", S., Невська, А. О., Pohosian, O. A., Goncharuk, K. O., Sofyna, D. F., Chernenko, O. O., Enterprise, M. P., Tronko, К. M., Kozhan, N. I., & Korol, A. R. (2024) · Oftalmologicheskii Zhurnal · doi:10.31288/oftalmolzh202412731 · Cited by 5 · see citing works
Sauer, L., Vitale, A. S., Jacoby, R., Hart, B., & Bernstein, P. S. (2023) · Retina · doi:10.1097/iae.0000000000003851 · Cited by 4 · see citing works
Advances in Computer-Aided Diagnosis of Diabetic RetinopathyPreprint
Chaturvedi, S. S., Gupta, K., Ninawe, V., & Prasad, P. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.09853 · Cited by 4 · see citing works
Roser, P., Grohmann, C., Aberle, J., Spitzer, M. S., & Kromer, R. (2021) · Diabetologie und Stoffwechsel · doi:10.1055/a-1484-9678 · Cited by 3 · see citing works
The Future of Automated Mobile Eye Diagnosis
Ludwig, C. A., Shan, M. X., Nguyen, N. P., Choi, D. H., Ku, V., & Lam, C. (2016) · Journal of Mobile Technology in Medicine · doi:10.7309/jmtm.5.2.7 · Cited by 3 · see citing works
Dourandeesh, M., Akbari, M., Pourramzani, A., Alizadeh, Y., Leili, E. K., Shemshadi, A. H., & manesh, G. M. (2024) · International Ophthalmology · doi:10.1007/s10792-024-03022-y · Cited by 3 · see citing works
A Review of CAM-Based Visual Explanation on Diabetic Retinopathy
Persada, A. G., Ardiyanto, I., Sasongko, M. B., & Nugroho, H. A. (2026) · IEEE Access · doi:10.1109/access.2026.3656888 · Cited by 2 · see citing works
Diabetic retinopathy and choroidopathy: Pathophysiology, imaging findings, and treatment updates
Silpa‐archa, S., Tadarati, M., Chotcomwongse, P., & Ruamviboonsuk, P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15583-3.00016-0 · Cited by 2 · see citing works
Innovative Approaches in Delivery of Eye Care: Diabetic Retinopathy
Ting, D. S. W., Lamoureux, E. L., & Wong, T. Y. (2018) · Essentials in ophthalmology/Essentials ophthalmology · doi:10.1007/978-3-319-98014-0_9 · Cited by 2 · see citing works
Using artificial intelligence in diabetic retinopathy
Mohan, S., Gaur, R., & Raman, R. (2022) · IHOPE Journal of Ophthalmology · doi:10.25259/ihopejo_20_2022 · Cited by 2 · see citing works
Alhajim, D., Al-Shammari, A., & Oleiwi, A. K. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/160168 · Cited by 2 · see citing works
Luminosity Normalized Symmetric Deep Convolute Tubular Classifier for Diabetic Retinopathy DetectionPreprint
Muthusamy, D., & Palani, P. (2022) · Research Square · doi:10.21203/rs.3.rs-2129147/v1 · Cited by 2 · see citing works
Roser, P., Grohmann, C., Aberle, J., Spitzer, M. S., & Kromer, R. (2021) · Diabetologie und Stoffwechsel · doi:10.1055/a-1521-7645 · Cited by 1 · see citing works
Selvin, G., & Joseph, A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-83204-5.00002-0 · Cited by 1 · see citing works
Artificial intelligence use in diabetes
Pelayes, D. E., Mendoza, J. A., & Folgar, A. M. (2022) · Latin American Journal of Ophthalmology · doi:10.25259/lajo_4_2022 · Cited by 1 · see citing works
Kgame, T., Hui-qun, W., & Geng, J. (2023) · Advances in Engineering Technology Research · doi:10.56028/aetr.5.1.327.2023 · Cited by 1 · see citing works
Sadjadi, R. (2021) · Preprints.org · doi:10.20944/preprints202108.0030.v1 · Cited by 1 · see citing works
Data analysis applied to diabetic retinopathy screening: Performance evaluation
Antal, B., Tavares, M. K. G. S., Kovács, L. L., Harangi, B., Lázár, I., Nagy, B., Kovács, G., Szakács, J., Tóth, J. A., Pető, T., et al. (2018) · Az Eszterházy Károly Tanárképző Főiskola tudományos közleményei. Tanulmányok a matematikai tudományok köréből/Az Eszterházy Károly Főiskola tudományos közleményei. Tanulmányok a matematikai tudományok köréből/Annales mathematicae et informaticae · doi:10.33039/ami.2018.10.002
Liu, Y., Yang, L., Phene, S., & Peng, L. H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00013-2
Digital Applications for Eye-Health Screening of Children: Challenges, Opportunities and Solutions
Asghar, I., Egaji, O. A., & Griffiths, M. (2021) · International Conference on Engineering and Emerging Technologies (ICEET) · doi:10.1109/iceet53442.2021.9659695
Artificial Intelligence Aiding Medical Diagnosis Focusing on Diabetic Retinopathy
Juneja, S., Bali, A., & Bali, N. (2022) · Advances in medical diagnosis, treatment, and care (AMDTC) book series · doi:10.4018/978-1-6684-2304-2.ch003
Inteligencia Artificial. Una Nueva Frontera en el Diagnóstico de la Retinopatía Diabética
Pelayes, D. E., Cotic, M., & Folgar, A. M. (2023) · Highlights of Vitreoretina · doi:10.5005/hov-10102-16303
Olawoye, O. O., Ha, T. H., Pham, N., Nguyen, L., Cherwek, D. H., Fowobaje, K. R., Ross, C. M., Coote, M., Chan, V. F., Kahook, M. Y., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-076623
Revolutionizing Diabetic Retinopathy Detection: A Comprehensive Review of AI Approaches
Imane, M., Abdelkader, H., & Boualem, M. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60632-8_33
Rogers, M., Kaggwa, F., Arunga, S., & Wasswa, W. (2025) · Cureus Journal of Computer Science. · doi:10.7759/s44389-025-07263-5
Bascaran, C., Mwangi, N., D’Esposito, F., Gordon, I., Ulloa, J. A. L., Mdala, S. Y., Ramke, J., Evans, J. R., & Burton, M. J. (2021) · Figshare · doi:10.6084/m9.figshare.13515581
Bascaran, C., Mwangi, N., D’Esposito, F., Gordon, I., Ulloa, J. A. L., Mdala, S. Y., Ramke, J., Evans, J. R., & Burton, M. J. (2021) · Figshare · doi:10.6084/m9.figshare.13515584
Bascaran, C., Mwangi, N., D’Esposito, F., Gordon, I., Ulloa, J. A. L., Mdala, S. Y., Ramke, J., Evans, J. R., & Burton, M. J. (2021) · Figshare · doi:10.6084/m9.figshare.13515587
AI and Diabetic Retinopathy: Methods and Results
Esserkassi, B., Eddarouich, S., & Bourouhou, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-14430-0_12
Cleland, C. R., Bascarán, C., Makupa, W. U., Shilio, B. R., Tufail, A., Egan, C. A., Fajtl, J., Olvera‐Barrios, A., Rudnicka, A. R., Owen, C. G., et al. (2026) · Diabetes Care · doi:10.2337/dc26-0572
Self-citationOkekunle, A. P., Asowata, O. J., Fakunle, A. G., Omoleye, O. J., Olaiya, M. T., Kumuthini, J., Morsy, M. I., Caiazzo, E., Kinengyere, A. A., Ajuwon, G. A., et al. (2026) · Communications Medicine · doi:10.1038/s43856-025-01272-0
Gupta, E., Aung, M. H., & Bowden, E. C. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15155827
Detección de retinopatía diabética a través de inteligencia artificial y valoración de fondo de ojo
Lugo-Sánchez, J. M., Seefoó-Jarquín, P., Estrada-Palacios, D., Lugo‐Sánchez, L. A., & López-Bonilla, X. (2026) · Revista Mexicana de Oftalmología · doi:10.24875/rmo.m26000322
Detection of diabetic retinopathy through artificial intelligence and fundus assessment
Lugo-Sánchez, J. M., Seefoó-Jarquín, P., Estrada-Palacios, D., Lugo‐Sánchez, L. A., & López-Bonilla, X. (2026) · Revista Mexicana de Oftalmología (English Edition) · doi:10.24875/rmoe.m26000319
- Authors
- Hansen, M. B., Abràmoff, M. D., Folk, J. C., Mathenge, W., Bastawrous, A., & Peto, T.
- Affiliations
- Moorfields Eye Hospital NHS Foundation Trust (GB)
- University of Iowa (US)
- London School of Hygiene & Tropical Medicine (GB)
- Corresponding author
- Hansen, Morten B.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-10-01
- Volume
- 10
- Issue
- 10
- Pages
- e0139148
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4591009
- OpenAlex
- W2114785239
- Cited by
- 93
- References
- 18
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 11 · 2023: 12 · 2022: 10 · 2021: 12 · 2020: 5 · 2019: 8 · 2018: 11 · 2017: 6 · 2016: 6
- FWCI
- 6.21
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- University of Southern Denmark Research Portal (University of Southern Denmark) (submittedVersion)
- Funders
- Arnold and Mabel Beckman Initiative for Macular Research
- Synoptik-Fonden
- National Institute for Health and Care Research
- Department for International Development
- Syddansk Universitet
- Familien Hede Nielsens Fond
- Moorfields Eye Hospital NHS Foundation Trust
- National Institutes of Health (5r01ey017066-04)
- Medical Research Council
- Biomedical Research Council
- National Eye Institute (R01-EY017066, R01EY018853)
- 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, automated retinal analysis, diabetic eye disease, sensitivity and specificity, retinopathy severity, digital retinal imaging
- MeSH terms
- Automation, Diabetic Retinopathy, Humans, Image Processing, Computer-Assisted, Kenya, Retina, Software, Molecular Imaging
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
