Artificial intelligence for diabetic retinopathy screening: a review
Open accessAbstract
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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AI for medical diagnosis holds potential, demonstrating high performance in detecting various diseases.
“Artificial intelligence (AI) for medical diagnosis holds potential, demonstrating high performance in detecting various diseases.57”
From Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images (2024) · cites this paper as [7]
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“state that even if AI assessment systems increase in quality, clinical experts will still be required to assess atypical situations and low-quality images, as well as for quality monitoring [21].”
BackgroundPredicting Keratoconus Progression and Need for Corneal Crosslinking Using Deep Learning
Kato, N., Masumoto, H., Tanabe, M., Sakai, C., Negishi, K., Torii, H., Tabuchi, H., & Tsubota, K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040844 · Cited by 45 · see citing works
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The upcoming role of Artificial Intelligence (AI) for retinal and glaucomatous diseases
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“19 More recently, there has been a signi fi cant advancement in smartphone-based AI.”
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“…and policy development. multiple studies have develop and validated the use of AI algorithms on retina imaging for various applications including the diagnosis and management of conditions such as diabetic retinopathy, 8 glaucoma, 9 age-related macular degeneration, 9 and other retinal diseases.”
Self-citationArtificial 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
“IDP specificity was 70%, and IDP sensitivity was 86.7% [ 60,62-64 ].”
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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) · Clinical ophthalmology · doi:10.2147/opth.s348479 · Cited by 36 · see citing works
“The SiDRP started to integrate AI algorithms into their workflow with the ultimate goal of fully automating the screening process, increasing efficiency, and reducing the cost for the whole program.(118) One proven application for AI within healthcare is clinical decision support.”
Artificial Intelligence Meets Neuro-Ophthalmology
Leong, Y.-Y., Vasseneix, C., Finkelstein, M. T., Miléa, D., & Najjar, R. P. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000512 · Cited by 36 · see citing works
“In ophthalmology, AI algorithms have been developed for the detection of diabetic retinopathy, 9,10 glaucoma, 11–13 age-related macular degeneration, 14–16 and retinopathy of prematurity.”
BackgroundDiabetic retinopathy detection using supervised and unsupervised deep learning: a review study
Naz, H. U., Ahuja, N. J., & Nijhawan, R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10770-x · Cited by 39 · see citing works
Novel Approaches for Early Detection of Retinal Diseases Using Artificial Intelligence
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“Ultimately, while AI devices hold great promise in alleviating the global burden of DR screening, addressing additional knowledge gaps is imperative to harness the full potential of this emerging technology [19].”
BackgroundUrsin, F., Timmermann, C., Orzechowski, M., & Steger, F. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.695217 · Cited by 32 · see citing works
Global trends and performances in diabetic retinopathy studies: A bibliometric analysis
Xiao, H., Tang, J., Zhang, F., Liu, L., Zhou, J. J., Chen, M., Li, M., Wu, X., Nie, Y., & Duan, J. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1128008 · Cited by 33 · see citing works
“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).”
BackgroundGrzybowski, A., Rao, D. P., Brona, P., Negiloni, K., Krzywicki, T., & Savoy, F. M. (2023) · Ophthalmic Research · doi:10.1159/000534098 · Cited by 33 · see citing works
“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-citationMunishKhanna, Singh, L. K., & Garg, H. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16236-6 · Cited by 32 · see citing works
“One alternative remedy for the aforementioned problems is automated DR screening, which is very effective, inexpensive, and requires fewer medical professionals [26].”
RetractedRadiomics-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 · see citing works
Aging under endocrine hormone regulation
Xing, Y., Fan, X., Wang, K., & Zhang, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1223529 · Cited by 32 · see citing works
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“Some are directed to provide only DR screening aspect and do not discuss the multi-classification aspect [23, 24].”
Gonçalves, M. B., Nakayama, L., Ferraz, D., Faber, H., Korot, E., Malerbi, F., Regatieri, C., Maia, M., Celi, L., Keane, P., et al. (2023) · Eye · doi:10.1038/s41433-023-02717-3 · Cited by 32 · see citing works
“Recently, artificial intelligence (AI) and deep learning (DL) algorithms have been reported to achieve robust performance in detecting DR from retinal photographs, representing a promising tool to manage the large amount of image data generated by screening programs [2, 6–9].”
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
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 · see citing works
Zhang, Q., Sampani, K., Xu, M., Cai, S.-Z., Deng, Y., Li, H., Sun, J. K., & Karniadakis, G. (2021) · Translational Vision Science & Technology · doi:10.1167/tvst.11.8.7 · Cited by 38 · see citing works
“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].”
BackgroundDoğan, M. E., Bilgin, A., Sarı, R., Bulut, M., Akar, Y., & Aydemir, M. (2024) · Eye · doi:10.1038/s41433-024-03000-9 · Cited by 24 · see citing works
“[7]).”
Impact and Trends in Global Ophthalmology
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“, Coimbra, Portugal), Singapore SERI-NUS (Singapore Eye Research Institute – National University of Singapore, Singapore), the Bosch DR algorithm (Robert Bosch GmbH, Bangalore, India), RetinaLyze® (RetinaLyze system A/S, Hellerup, Denmark)) [25].”
MethodSaiviroonporn, P., Rodbangyang, K., Tongdee, T., Chaisangmongkon, W., Yodprom, P., Siriapisith, T., Wonglaksanapimon, S., & Thiravit, P. (2021) · BMC Medical Imaging · doi:10.1186/s12880-021-00625-0 · Cited by 29 · see citing works
Porporato, N., Tun, T. A., Baskaran, M., Wong, D. W. K., Husain, R., Fu, H., Sultana, R., Perera, S., Schmetterer, L. F., & Aung, T. (2021) · British Journal of Ophthalmology · doi:10.1136/bjophthalmol-2020-318275 · Cited by 29 · see citing works
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 · see citing works
“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…”
BackgroundMaloca, 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 · see citing works
Ryu, G., Lee, K., Park, D.-G., Kim, I., Park, S. H., & Sagong, M. (2022) · Translational Vision Science & Technology · doi:10.1167/tvst.11.2.39 · Cited by 28 · see citing works
Diabetic Retinopathy Classification Using Swin Transformer with Multi Wavelet
Dihin, R. A., AlShemmary, E. N., & Al‐Jawher, W. A. M. (2023) · Journal of Kufa for Mathematics and Computer · doi:10.31642/jokmc/2018/100225 · Cited by 28 · see citing works
“These statistics have become a global epidemic [2].”
BackgroundKubin, 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 · see citing works
“…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].”
BackgroundMachine 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 · see citing works
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 · see citing works
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 26 · see citing works
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 · see citing works
“ML models have proved to be robust for the diagnosis of several diseases [39–43], including ophthalmology-related ones [4,14].”
BackgroundRuan, 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 · see citing works
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 25 · see citing works
“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.”
BackgroundRetinal 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 · see citing works
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 · see citing works
“New approaches using AI algorithms evaluate these fundus images with regard to the presence and stage of diabetic retinopathy [3, 4].”
MethodMehra, 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 · see citing works
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 24 · see citing works
Hybrid CNN-LSTM for Predicting Diabetes: A Review
Soltanizadeh, S., & Naghibi, S. S. (2023) · Current Diabetes Reviews · doi:10.2174/0115733998261151230925062430 · 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
“Conventional screening with trained human graders is often costly, time-consuming, and challenging to scale [5,6].”
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 · see citing works
Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy
Zhang, Y., Zhao, H., Lin, J., Wu, S., Liu, X., Zhang, H.-D., Shao, Y., & Yang, W. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.774344 · Cited by 24 · see citing works
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 · see citing works
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 · see citing works
“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 23 · see citing works
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 · see citing works
Dong, X., Du, S., Zheng, W., Cai, C., Liu, H., & Zou, J. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.883462 · Cited by 23 · see citing works
“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 · see citing works
“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.”
MethodEyeArt 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 · see citing works
“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].”
Gao, W., Fan, B., Fang, Y., & Song, N. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107854 · Cited by 23 · see citing works
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 · see citing works
“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…”
BackgroundArtificial 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 22 · see citing works
“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
- Venue
- Eye
- Published
- 2019-09-05
- Volume
- 34
- Issue
- 3
- Pages
- 451-460
- ISSN
- 0078-5334 (ISSN-L)
- 0950-222X
- 1476-5454
- 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
- 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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