Cited by 376Retinal Imaging and AnalysisRetinal Diseases and TreatmentsAI in cancer detection

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Artificial intelligence for diabetic retinopathy screening: a review

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Grzybowski, Andrzej, Brona, Piotr, Lim, Gilbert, Ruamviboonsuk, Paisan, Tan, Gavin S. W., Abramoff, Michael, Ting, Daniel S. W.·2020·Eye

DOI 10.1038/s41433-019-0566-0PMC PMC7055592

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Abstract

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.

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“However, an ethical review of a large number of original data is required for the research of AI algorithms, and the application of clinical care must be properly standardized and deployed (67, 68).”
Background
Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence
Grzybowski, A., Rao, D. P., Brona, P., Negiloni, K., Krzywicki, T., & Savoy, F. M. (2023) · Ophthalmic Research · doi:10.1159/000534098 · Cited by 33
“There are only a few established nationwide DR screening programs, and overall DR screening services remain inadequate in most of the developing world and even some developed countries [1].”
BackgroundSelf-citation
RETRACTED ARTICLE: A novel approach for human diseases prediction using nature inspired computing & machine learning approach
MunishKhanna, Singh, L. K., & Garg, H. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16236-6 · Cited by 33
“One alternative remedy for the aforementioned problems is automated DR screening, which is very effective, inexpensive, and requires fewer medical professionals [26].”
Retracted
Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis
Carrera-Escalé, L., Benali, A., Rathert, A.-C., Martín, R., Bernal-Morales, C., Alé-Chilet, A., Barraso, M., Marín-Martínez, S., Feu-Basilio, S., Rosinés-Fonoll, J., et al. (2022) · Ophthalmology Science · doi:10.1016/j.xops.2022.100259 · Cited by 32
Aging under endocrine hormone regulation
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Comparative Analysis of Diabetic Retinopathy Classification Approaches Using Machine Learning and Deep Learning Techniques
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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 31
Deep Learning for Anterior Segment Optical Coherence Tomography to Predict the Presence of Plateau Iris
Wanichwecharungruang, B., Kaothanthong, N., Pattanapongpaiboon, W., Chantangphol, P., Seresirikachorn, K., Srisuwanporn, C., Parivisutt, N., Grzybowski, A. E., Theeramunkong, T., & Ruamviboonsuk, P. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.10.1.7 · Cited by 31
AOSLO-net: A Deep Learning-Based Method for Automatic Segmentation of Retinal Microaneurysms From Adaptive Optics Scanning Laser Ophthalmoscopy Images
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“With the recent advances in the computational power of graphics processing units (GPUs), deep convolutional neural networks (DCNNs) have become a widely used tool for eﬃcient DR screening [29].”
Background
Head to head comparison of diagnostic performance of three non-mydriatic cameras for diabetic retinopathy screening with artificial intelligence
Doğan, M. E., Bilgin, A., Sarı, R., Bulut, M., Akar, Y., & Aydemir, M. (2024) · Eye · doi:10.1038/s41433-024-03000-9 · Cited by 24
“[7]).”
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Method
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Towards ‘automated gonioscopy’: a deep learning algorithm for 360° angle assessment by swept-source optical coherence tomography
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Potential Ocular Biomarkers for Early Detection of Alzheimer’s Disease and Their Roles in Artificial Intelligence Studies
Chaitanuwong, P., Singhanetr, P., Chainakul, M., Arjkongharn, N., Ruamviboonsuk, P., & Grzybowski, A. E. (2023) · Neurology and Therapy · doi:10.1007/s40120-023-00526-0 · Cited by 28
“AI algorithms can extract both known and unknown features from images and provide a reliable diagnosis without the need for manual feature identification, as has been shown for eye diseases where AI has been used on retinal images to identify such eye diseases as age-related macular degeneration [7], glaucoma [8], pap…”
Background
Unraveling the deep learning gearbox in optical coherence tomography image segmentation towards explainable artificial intelligence
Maloca, P. M., Müller, P. L., Lee, A., Tufail, A., Balaskas, K., Niklaus, S., Kaiser, P., Suter, S. K., Zarranz‐Ventura, J., Egan, C. A., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01697-y · Cited by 28
A Deep Learning Algorithm for Classifying Diabetic Retinopathy Using Optical Coherence Tomography Angiography
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Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet
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Background
Comparison of 21 artificial intelligence algorithms in automated diabetic retinopathy screening using handheld fundus camera
Kubin, A., Huhtinen, P., Ohtonen, P. P., Keskitalo, A., Wirkkala, J., & Hautala, N. M. (2024) · Annals of Medicine · doi:10.1080/07853890.2024.2352018 · Cited by 28
“…the knowledge and understanding of the possibilities and limitations of ai solutions is crucial for their successful use in a real-world setting: patient acceptability, data privacy, data protection, regulations, including medico-legal aspects, are among the issues that need to be considered [23].”
Background
Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review
Alawad, M., Aljouie, A., Alamri, S., Alghamdi, M., Alabdulkader, B., Alkanhal, N., & Almazroa, A. A. (2022) · Dove Medical Press (Taylor and Francis Group) · Cited by 27
Screening Diabetic Retinopathy Using an Automated Retinal Image Analysis System in Independent and Assistive Use Cases in Mexico: Randomized Controlled Trial
Noriega-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
Therapeutic Screening of Herbal Remedies for the Management of Diabetes
Balbaa, M., El‐Zeftawy, M., & Abdulmalek, S. A. (2021) · Molecules · doi:10.3390/molecules26226836 · Cited by 28
Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy—Application of Sensor Arrays and Machine Learning
Faura, G., Boix‐Lemonche, G., Holmeide, A. K., Verkauskienė, R., Volke, V., Sokolovska, J., & Petrovski, G. (2022) · Sensors · doi:10.3390/s22030718 · Cited by 26
“ML models have proved to be robust for the diagnosis of several diseases [39–43], including ophthalmology-related ones [4,14].”
Background
A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening
Ruan, S., Hu, W., Jia, H., Wang, S., Song, M.-L., Shen, M.-X., Luo, D., Ye, T., & Wang, F. (2022) · International Journal of Ophthalmology · doi:10.18240/ijo.2022.04.16 · Cited by 27
Research Progress and Prospects of Intelligent Diabetes Monitoring Systems: A Review
Zou, Y., Chu, Z., Yang, T., Guo, J., & Li, D. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3411570 · Cited by 27
“Grzybowski et al. [228] provide a detailed overview of the use of AI in diabetic retinopathy screening and discuss some of the challenges that need to be addressed for future research.”
Background
Retinal vascular tortuosity: Mechanisms and measurements
Vilela, M. A. P., Amaral, C. E. V., & Ferreira, M. A. T. (2020) · European Journal of Ophthalmology · doi:10.1177/1120672120979907 · Cited by 25
Distribution of Pupil Size and Associated Factors: Results from the Population-Based Gutenberg Health Study
Kiel, M., Grabitz, S. D., Hopf, S., Koeck, T., Wild, P. S., Schmidtmann, I. M., Lackner, K. J., Münzel, T. F., Beutel, M. E., Pfeiffer, N., et al. (2022) · Journal of Ophthalmology · doi:10.1155/2022/9520512 · Cited by 25
“New approaches using AI algorithms evaluate these fundus images with regard to the presence and stage of diabetic retinopathy [3, 4].”
Method
Diabetic Retinopathy Telemedicine Outcomes With Artificial Intelligence-Based Image Analysis, Reflex Dilation, and Image Overread
Mehra, A. A., Softing, A., Guner, M. K., Hodge, D. O., & Barkmeier, A. J. (2022) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2022.08.008 · Cited by 24
Development and validation of an artificial intelligence based screening tool for detection of retinopathy of prematurity in a South Indian population
Rao, D. P., Savoy, F. M., Tan, J. Z. E., Fung, B., Bopitiya, C. M., Sivaraman, A., & Vinekar, A. S. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1197237 · Cited by 28
Hybrid CNN-LSTM for Predicting Diabetes: A Review
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Real-World Evaluation of AI-Driven Diabetic Retinopathy Screening in Public Health Settings: Validation and Implementation Study
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
“Conventional screening with trained human graders is often costly, time-consuming, and challenging to scale [5,6].”
Evidence-based Canadian guidelines for tele-retina screening for diabetic retinopathy: recommendations from the Canadian Retina Research Network (CR2N) Tele-Retina Steering Committee
Boucher, M. C., Qian, J., Brent, M. H., Wong, D. T., Sheidow, T., Duval, R., Kherani, A., Dookeran, R. I., Maberley, D., Samad, A., et al. (2020) · Canadian Journal of Ophthalmology · doi:10.1016/j.jcjo.2020.01.001 · Cited by 24
Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy
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Artificial intelligence in uveitis: A comprehensive review
Nakayama, L. F., Ribeiro, L. Z., Dychiao, R. G., Zamora, Y. F., Regatieri, C. V., Celi, L. A., Silva, P., Sobrin, L., & Belfort, R. (2023) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2023.02.007 · Cited by 24
Diabetic retinopathy disease detection using shapley additive ensembled densenet-121 resnet-50 model
Mary, A. R., & Kavitha, P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18309-6 · Cited by 23
“Diabetic retinopathy is a common vision loss among employed people in urbanized countries caused by the problem of diabetic mellitus [1].”
Evaluation and Regulation of Artificial Intelligence Medical Devices for Clinical Decision Support
Weissman, G. E. (2025) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-103123-095824 · Cited by 31
Recent Advances and Clinical Application of Color Scanning Laser Ophthalmoscope
Terasaki, H., Sonoda, S., Tomita, M., & Sakamoto, T. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040718 · Cited by 23
Evaluation of an Artificial Intelligence System for the Detection of Diabetic Retinopathy in Chinese Community Healthcare Centers
Dong, X., Du, S., Zheng, W., Cai, C., Liu, H., & Zou, J. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.883462 · Cited by 24
“IDx-DR (Digital Diagnostics, Coralville, USA), Selena (EyRIS, Singapore), Google Inception, and Robert Bosch GmbH (Gerlingen-Schillerhöhe, Germany) are the most widely used systems (12).”
Review on diabetic retinopathy with deep learning methods
Shekar, S. C., Satpute, N., & Gupta, A. (2021) · Journal of Medical Imaging · doi:10.1117/1.jmi.8.6.060901 · Cited by 19
“120, the commercially available latest DR screening technologies are summarized, which include IDx-DR, RetmarkerDR, EyeArt, Google, Singapore SERI-NUS, Bosch DR algo-rithm, and Retinalyze.”
Method
EyeArt artificial intelligence analysis of diabetic retinopathy in retinal screening events
Vought, R., Vought, V., Shah, M., Szirth, B., & Bhagat, N. (2023) · International Ophthalmology · doi:10.1007/s10792-023-02887-9 · Cited by 23
“DR screening programs, typically involving fundus photography and review by a specially—telemedicine grader, have become prevalent to address increasing rates of DR and maintain adequate community surveillance [8].”
Lightweight and multi-lesion segmentation model for diabetic retinopathy based on the fusion of mixed attention and ghost feature mapping
Gao, W., Fan, B., Fang, Y., & Song, N. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107854 · Cited by 23
Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images
Tabuchi, H., Engelmann, J., Maeda, F., Nishikawa, R., Nagasawa, T., Yamauchi, T., Tanabe, M., Akada, M., Kihara, K., Nakae, Y., et al. (2024) · British Journal of Ophthalmology · doi:10.1136/bjo-2023-324923 · Cited by 22
“4 7 However, challenges such as decreased performance on unseen imaging modalities and the commercially available AI diagnosis, which is difficult for medical insurance systems to cover, 8 need to be addressed to fully harness AI’s potential and ensure its accessibility and effectiveness for…”
Background
Artificial Intelligence in Uveitis: Innovations in Diagnosis and Therapeutic Strategies
Murugan, S., Sanjay, S., Somanath, A., Mahendradas, P., Patil, A., Kaur, K., & Gurnani, B. (2024) · Clinical ophthalmology · doi:10.2147/opth.s495307 · Cited by 24
“20 Abellanas et al 21 in their review of AI in uveitis noted that presently the performance of AI models were poor with limited datasets and a lack of validation studies and publicly available data and codes.”

Authors: Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., & Ting, D. S. W.
Affiliations: Foundation for the Development of Ophthalmology (PL)
University of Warmia and Mazury in Olsztyn (PL)
National University of Singapore (SG)
Rajavithi Hospital (TH)
Corresponding author: Ting, Daniel S. W.
ORCID: Grzybowski, Andrzej
Brona, Piotr
Lim, Gilbert
Ruamviboonsuk, Paisan
Tan, Gavin S. W.
Ting, Daniel S. W.
Venue: Eye
Publisher: Springer Science and Business Media LLC
Published: 2019-09-05
Volume: 34
Issue: 3
Pages: 451-460
ISSN: 0078-5334 (ISSN-L)
0950-222X
1476-5454
DOI: 10.1038/s41433-019-0566-0
PMC: PMC7055592
OpenAlex: W2972246881
Cited by: 376
References: 36
Citations by year: 2026: 34 · 2025: 69 · 2024: 70 · 2023: 76 · 2022: 63 · 2021: 40 · 2020: 21 · 2019: 2
FWCI: 4.72
Type: journal article
Language: en
Update notices: correction (2019-12-10)
Open access: bronze
doi.org
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Eye (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: diabetic retinopathy, retinopathy screening, artificial intelligence, automated detection algorithm, deep learning, machine learning, medicolegal issues, ethics, clinical deployment
MeSH terms: Machine Learning, Algorithms, Artificial Intelligence, Diabetes Mellitus, Diabetic Retinopathy, Humans, Mass Screening

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Artificial intelligence for diabetic retinopathy screening: a review

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