Grading Diabetic Retinopathy from Stereoscopic Color Fundus Photographs—An Extension of the Modified Airlie House Classification

The Measurement of Observer Agreement for Categorical Data

Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148

Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit

Cohen, J. (1968) · Psychological Bulletin · doi:10.1037/h0026256 · Cited by 8,736

The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus

(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667

Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study.

Ohkubo, Y., Kishikawa, H., Araki, E., Miyata, T., Isami, S., Motoyoshi, S., Kojima, Y., Furuyoshi, N., & Shichiri, M. (1995) · Diabetes Research and Clinical Practice · doi:10.1016/0168-8227(95)01064-k · Cited by 3,079

The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview

Nathan, D. M., & for the DCCT/EDIC Research Group (2014) · Diabetes Care · doi:10.2337/dc13-2112 · Cited by 1,832

Effect of intensive diabetes treatment on the development and progression of long-term complications in adolescents with insulin-dependent diabetes mellitus: Diabetes Control and Complications Trial

(1994) · The Journal of Pediatrics · doi:10.1016/s0022-3476(94)70190-3 · Cited by 1,654

Effect of fenofibrate on the need for laser treatment for diabetic retinopathy (FIELD study): a randomised controlled trial

Keech, A., Mitchell, P., Summanen, P., O'Day, J., Davis, T., Moffitt, M., Taskinen, M.-R., Simes, R., Tse, D., Williamson, E., et al. (2007) · The Lancet · doi:10.1016/s0140-6736(07)61607-9 · Cited by 1,109

Diabetic retinopathy: global prevalence, major risk factors, screening practices and public health challenges: a review

Ting, D. S. W., Cheung, G. C. M., & Wong, T. Y. (2016) · Clinical & Experimental Ophthalmology · doi:10.1111/ceo.12696 · Cited by 1,066

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy

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

Early Worsening of Diabetic Retinopathy in the Diabetes Control and Complications Trial

(1998) · Archives of Ophthalmology · doi:10.1001/archopht.116.7.874 · Cited by 509

Effect of candesartan on progression and regression of retinopathy in type 2 diabetes (DIRECT-PROTECT 2): A randomised placebo-controlled trial

Sjølie, A. K., Klein, R., Porta, M., Orchard, T., Fuller, J., Parving, H. H., Bilous, R., & Chaturvedi, N. (2008) · The Lancet · doi:10.1016/s0140-6736(08)61411-7 · Cited by 435

EyePACS: An Adaptable Telemedicine System for Diabetic Retinopathy Screening

Cuadros, J., & Bresnick, G. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300315 · Cited by 390

Adenoviral Vector-Delivered Pigment Epithelium-Derived Factor for Neovascular Age-Related Macular Degeneration: Results of a Phase I Clinical Trial

Campochiaro, P. A., Nguyen, Q. D., Shah, S. M., Klein, M. L., Holz, E., Frank, R. N., Saperstein, D. A., Gupta, A., Stout, J. T., Macko, J., et al. (2006) · Human Gene Therapy · doi:10.1089/hum.2006.17.167 · Cited by 343

Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images

Zhang, K., Liu, X., Xu, J., Yuan, J., Cai, W., Chen, T., Wang, K., Gao, Y., Nie, S., Xu, X., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00745-6 · Cited by 324

Glycemic Thresholds for Diabetes-Specific Retinopathy

Colagiuri, S., Lee, C. M. Y., Wong, T. Y., Balkau, B., Shaw, J. E., Borch-Johnsen, K., & the DETECT-2 Collaboration Writing Group (2011) · Diabetes Care · doi:10.2337/dc10-1206 · Cited by 300

Predicting the risk of developing diabetic retinopathy using deep learning

Bora, A., Balasubramanian, S., Babenko, B., Virmani, S., Venugopalan, S., Mitani, A., de Oliveira Marinho, G., Cuadros, J., Ruamviboonsuk, P., Corrado, G. S., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30250-8 · Cited by 270

Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy

Ipp, E., Liljenquist, D., Bode, B., Shah, V. N., Silverstein, S., Regillo, C. D., Lim, J. I., Sadda, S., Domalpally, A., Gray, G., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.34254 · Cited by 256

Long-term effects of a randomised trial of a 6-year lifestyle intervention in impaired glucose tolerance on diabetes-related microvascular complications: the China Da Qing Diabetes Prevention Outcome Study

Gong, Q., Gregg, E. W., Wang, J., An, Y., Zhang, P., Yang, W., Li, H., Li, H., Jiang, Y., Shuai, Y., et al. (2011) · Diabetologia · doi:10.1007/s00125-010-1948-9 · Cited by 251

Reduced progression of diabetic microvascular complications with islet cell transplantation compared with intensive medical therapy

Thompson, D. M., Meloche, M., Ao, Z., Paty, B., Keown, P., Shapiro, R. J., Ho, S., Worsley, D., Fung, M., Meneilly, G., et al. (2011) · Transplantation · doi:10.1097/tp.0b013e31820437f3 · Cited by 213

Prevalence, incidence and future projection of diabetic eye disease in Europe: A systematic review and meta-analysis

Li, J. Q., Welchowski, T., Schmid, M., Letow, J., Wolpers, C., Pascual-Camps, I., Holz, F. G., & Finger, R. P. (2020) · European Journal of Epidemiology · doi:10.1007/s10654-019-00560-z · Cited by 179

Validation of Smartphone Based Retinal Photography for Diabetic Retinopathy Screening

Rajalakshmi, R., Arulmalar, S., Usha, M., Prathiba, V., Kareemuddin, K. S., Anjana, R. M., & Mohan, V. (2015) · PLOS ONE · doi:10.1371/journal.pone.0138285 · Cited by 173

Sympathetic dysfunction in type 1 diabetes

Pop-Busui, R., Kirkwood, I., Schmid, H., Marinescu, V., Schroeder, J., Larkin, D., Yamada, E., Raffel, D. M., & Stevens, M. J. (2004) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2004.09.033 · Cited by 170

The sensitivity and specificity of nonmydriatic digital stereoscopic retinal imaging in detecting diabetic retinopathy

Ahmed, J., Ward, T. P., Bursell, S.-E., Aiello, L. M., Cavallerano, J. D., & Vigersky, R. A. (2006) · Diabetes Care · doi:10.2337/dc06-0295 · Cited by 152

Aortic Distensibility and Retinal Arteriolar Narrowing: The Multi-Ethnic Study of Atherosclerosis

Cheung, N., Sharrett, A. R., Klein, R., Criqui, M. H., Islam, F. M. A., Macura, K. J., Cotch, M. F., Klein, B. E. K., & Wong, T. Y. (2007) · Hypertension · doi:10.1161/hypertensionaha.107.091926 · Cited by 124

Comprehensive review of the effects of diabetes on ocular health

Skarbez, K., Priestley, Y., Hoepf, M., & Koevary, S. B. (2010) · Expert Review of Ophthalmology · doi:10.1586/eop.10.44 · Cited by 117

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

Inflammation, Complement Factor H, and Age-Related Macular Degeneration

Klein, R., Knudtson, M. D., Klein, B. E. K., Wong, T. Y., Cotch, M. F., Liu, K., Cheng, C. Y., Burke, G. L., Saad, M. F., Jacobs, D. R., et al. (2008) · Ophthalmology · doi:10.1016/j.ophtha.2008.03.021 · Cited by 106

Learning ensemble classifiers for diabetic retinopathy assessment

Saleh, E., Błaszczyński, J., Moreno, A., Valls, A., Romero-Aroca, P., de la Riva-Fernández, S., & Słowiński, R. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.09.006 · Cited by 97

Progression of retinopathy during pregnancy in type 1 diabetic women treated with insulin lispro

Loukovaara, S., Immonen, I., Teramo, K. A., & Kaaja, R. (2003) · Diabetes Care · doi:10.2337/diacare.26.4.1193 · Cited by 80

Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in south India

Amutha, A., Datta, M., Unnikrishnan, I. R., Anjana, R. M., Rema, M., Venkat Narayan, K. M., & Mohan, V. (2011) · Primary Care Diabetes · doi:10.1016/j.pcd.2011.04.003 · Cited by 80

Diabetic retinopathy screening using digital non-mydriatic fundus photography and automated image analysis

Hansen, A. B., Hartvig, N. V., Jensen, M. S., Borch‐Johnsen, K., Lund‐Andersen, H., & Larsen, M. (2004) · Acta Ophthalmologica Scandinavica · doi:10.1111/j.1600-0420.2004.00350.x · Cited by 67

Diagnostic test accuracy of diabetic retinopathy screening by physician graders using a hand-held non-mydriatic retinal camera at a tertiary level medical clinic

Piyasena, M. M. P. N., Yip, J. L. Y., MacLeod, D., Kim, M., & Gudlavalleti, V. S. M. (2019) · BMC Ophthalmology · doi:10.1186/s12886-019-1092-3 · Cited by 70

Optical coherence tomography grading reproducibility during the Comparison of Age-related Macular Degeneration Treatments Trials

DeCroos, F. C., Toth, C. A., Stinnett, S. S., Heydary, C. S., Burns, R., & Jaffe, G. J. (2012) · Ophthalmology · doi:10.1016/j.ophtha.2012.06.040 · Cited by 67

Validation of Automated Screening for Referable Diabetic Retinopathy With an Autonomous Diagnostic Artificial Intelligence System in a Spanish Population

Shah, A., Clarida, W., Amelon, R., Hernaez-Ortega, M. C., Navea, A., Morales-Olivas, J., Dolz-Marco, R., Verbraak, F., Jorda, P. P., van der Heijden, A. A., et al. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820906212 · Cited by 68

Do we have enough ophthalmologists to manage vision-threatening diabetic retinopathy? A global perspective

Teo, Z. L., Tham, Y.-C., Yu, M., Cheng, C.-Y., Wong, T. Y., & Sabanayagam, C. (2020) · Eye · doi:10.1038/s41433-020-0776-5 · Cited by 62

Accuracy and Reliability of Teleophthalmology for Diagnosing Diabetic Retinopathy and Macular Edema: A Review of the Literature

Whited, J. D. (2006) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2006.8.102 · Cited by 60

Thirty-Year Trends in Complications in U.S. Adults With Newly Diagnosed Type 2 Diabetes

Fang, M., & Selvin, E. (2021) · Diabetes Care · doi:10.2337/dc20-2304 · Cited by 59

Risk factors of chronic kidney disease among type 2 diabetic patients with longer duration of diabetes

Siddiqui, K., George, T. P., Joy, S. S., & Alfadda, A. A. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1079725 · Cited by 59

Racial disparities in the screening and treatment of diabetic retinopathy

Coney, J. M., & Scott, A. W. (2022) · Journal of the National Medical Association · doi:10.1016/j.jnma.2021.12.011 · Cited by 53

Effect of duration and burden of microvascular complications on mortality rate in type 1 diabetes: an observational clinical cohort study

Bjerg, L., Hulman, A., Carstensen, B., Charles, M., Witte, D. R., & Jørgensen, M. E. (2019) · Diabetologia · doi:10.1007/s00125-019-4812-6 · Cited by 39

Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging

Sobhi, N., Sadeghi-Bazargani, Y., Mirzaei, M., Abdollahi, M., Jafarizadeh, A., Pedrammehr, S., Alizadehsani, R., Tan, R.-S., Islam, S. M. S., & Acharya, U. R. (2025) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-025-01596-7 · Cited by 39

The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey

Elsharkawy, M., Elrazzaz, M., Sharafeldeen, A., Alhalabi, M., Khalifa, F., Soliman, A., Elnakib, A., Mahmoud, A., Ghazal, M., El-Daydamony, E., et al. (2022) · Sensors · doi:10.3390/s22093490 · Cited by 38

The Indian Health Service Primary Care-Based Teleophthalmology Program for Diabetic Eye Disease Surveillance and Management

Fonda, S. J., Bursell, S.-E., Lewis, D. G., Clary, D., Shahon, D., & Horton, M. B. (2020) · Telemedicine and e-Health · doi:10.1089/tmj.2019.0281 · Cited by 34

Development and External Validation of Machine Learning Models for Diabetic Microvascular Complications: Cross-Sectional Study With Metabolites

He, F., Ng Yin Ling, C., Nusinovici, S., Cheng, C.-Y., Wong, T. Y., Li, J., & Sabanayagam, C. (2024) · Journal of Medical Internet Research · doi:10.2196/41065 · Cited by 29

Prevalence and risk factors for diabetic retinopathy in the 40 to 80 year‐old population in Yazd, Iran: The Yazd Eye Study 伊朗亚兹德省40至80岁人群的糖尿病视网膜病变患病率以及危险因素：亚兹德眼病研究

Dehghan, M. H., Katibeh, M., Ahmadieh, H., Nourinia, R., & Yaseri, M. (2015) · Journal of Diabetes · doi:10.1111/1753-0407.12205 · Cited by 27

Light therapy for better mood and insulin sensitivity in patients with major depression and type 2 diabetes: a randomised, double-blind, parallel-arm trial

Brouwer, A., van Raalte, D. H., Diamant, M., Rutters, F., van Someren, E. J. W., Snoek, F. J., Beekman, A. T. F., & Bremmer, M. A. (2015) · BMC Psychiatry · doi:10.1186/s12888-015-0543-5 · Cited by 25

Diagnostic accuracy of smartphone-based artificial intelligence systems for detecting diabetic retinopathy: A systematic review and meta-analysis

Hasan, S. U., & Siddiqui, M. A. R. (2023) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2023.110943 · Cited by 20

Remote Tool-Based Adjudication for Grading Diabetic Retinopathy

Schaekermann, M., Hammel, N., Terry, M., Ali, T. K., Liu, Y., Basham, B., Campana, B., Chen, W., Ji, X., Krause, J., et al. (2019) · Translational Vision Science & Technology · doi:10.1167/tvst.8.6.40 · Cited by 19

Clinical Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success

Wolf, R. M., Channa, R., Lehmann, H. P., Abramoff, M. D., & Liu, T. Y. A. (2024) · Clinical Diabetes · doi:10.2337/cd23-0019 · Cited by 12