Artificial intelligence in retina
Open accessAbstract
Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML. The methods of AI-based retinal analyses are diverse and differ widely in their applicability, interpretability and reliability in different datasets and diseases. Fully automated AI-based systems have recently been approved for screening of diabetic retinopathy (DR). The overall potential of ML/DL includes screening, diagnostic grading as well as guidance of therapy with automated detection of disease activity, recurrences, quantification of therapeutic effects and identification of relevant targets for novel therapeutic approaches. Prediction and prognostic conclusions further expand the potential benefit of AI in retina which will enable personalized health care as well as large scale management and will empower the ophthalmologist to provide high qua…
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AI models have matched or surpassed human specialists in screening for diabetic retinopathy.
“Additionally, AI has shown great promise in screening for complications such as diabetic retinopathy, where AI models have matched or even surpassed the diagnostic capabilities of human specialists (17).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [17]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
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
Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., Yan, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.010 · Cited by 4,842 · 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
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works
Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775 · 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
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
Busbee, B. G., Ho, A. C., Brown, D. M., Heier, J. S., Suñer, I. J., Li, Z., Rubio, R. G., & Lai, P. (2013) · Ophthalmology · doi:10.1016/j.ophtha.2012.10.014 · Cited by 500 · see citing works
van Grinsven, M. J. J. P., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sanchez, C. I. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2526689 · Cited by 426 · see citing works
A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration
Schmidt-Erfurth, U., & Waldstein, S. M. (2016) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2015.07.007 · Cited by 390 · see citing works
Self-citationHan, S. S., Park, G. H., Lim, W., Kim, M. S., Na, J. I., Park, I., & Chang, S. E. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191493 · Cited by 305 · see citing works
Lost in thought—the limits of the human mind and the future of medicine
Obermeyer, Z., & Lee, T. H. (2017) · New England Journal of Medicine · doi:10.1056/nejmp1705348 · Cited by 252 · see citing works
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Cited by
Sorted by citations
The impact of artificial intelligence in medicine on the future role of the physician
Ahuja, A. S. (2019) · PeerJ · doi:10.7717/peerj.7702 · Cited by 789 · 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
Evaluating the Performance of ChatGPT in Ophthalmology
Antaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023) · Ophthalmology Science · doi:10.1016/j.xops.2023.100324 · Cited by 563 · see citing works
Pathophysiology of diabetic retinopathy: The old and the new
Kusuhara, S., Fukushima, Y., Ogura, S., Inoue, N., & Uemura, A. (2018) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2018.0182 · Cited by 250 · see citing works
Li, F., Chen, H., Liu, Z., Zhang, X.-D., Jiang, M.-S., Wu, Z.-Z., & Zhou, K.-Q. (2019) · Biomedical Optics Express · doi:10.1364/boe.10.006204 · Cited by 193 · see citing works
Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal OCT
Seebock, P., Orlando, J. I., Schlegl, T., Waldstein, S. M., Bogunovic, H., Klimscha, S., Langs, G., & Schmidt-Erfurth, U. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2919951 · Cited by 164 · see citing works
Self-citationAntaki, F., Touma, S., Milad, D., El-Khoury, J., & Duval, R. (2023) · doi:10.1101/2023.01.22.23284882 · Cited by 563 · 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
Lo, J., Yu, T. T., Ma, D., Zang, P., Owen, J. P., Zhang, Q., Wang, R. K., Beg, M. F., Lee, A. Y., Jia, Y., et al. (2021) · Ophthalmology Science · doi:10.1016/j.xops.2021.100069 · Cited by 105 · see citing works
Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
Huang, X., Wang, H., She, C., Feng, J., Liu, X., Hu, X., Chen, L., & Tao, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.946915 · Cited by 115 · see citing works
Lin, S., Ma, Y., Xu, Y., Lu, L., He, J., Zhu, J., Peng, Y., Yu, T., Congdon, N., & Zou, H. (2023) · JMIR Public Health and Surveillance · doi:10.2196/41624 · Cited by 52 · see citing works
Yang, Q., Bee, Y. M., Lim, C. C., Sabanayagam, C., Yim-Lui Cheung, C., Wong, T. Y., Ting, D. S. W., Lim, L.-L., Li, H., He, M., et al. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2025.103089 · Cited by 38 · see citing works
Young, R. A., Martin, C. M., Sturmberg, J. P., Hall, S., Bazemore, A., Kakadiaris, I. A., & Lin, S. (2024) · The Journal of the American Board of Family Medicine · doi:10.3122/jabfm.2023.230219r1 · Cited by 17 · see citing works
Barriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review
Assadullah, M. M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3530598 · Cited by 13 · see citing works
Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works
Sun, C., Wang, R., Zhao, L., Han, L., Ma, S., Liang, D., Wang, L., Tuo, X., Zhang, Y., Zhong, D., et al. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0142-oa · Cited by 2 · see citing works
- Authors
- Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H.
- Affiliations
- Medical University of Vienna (AT)
- Corresponding author
- Schmidt-Erfurth, Ursula
- ORCID
- Venue
- Progress in Retinal and Eye Research
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 67
- Pages
- 1-29
- ISSN
- 1350-9462 (ISSN-L)
- OpenAlex
- W2886801379
- Cited by
- 779
- References
- 16
- Citations by year
- 2026: 70 · 2025: 110 · 2024: 107 · 2023: 104 · 2022: 120 · 2021: 115 · 2020: 103 · 2019: 47 · 2018: 2
- FWCI
- 10.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Progress in Retinal and Eye Research (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Christian Doppler Laboratory for Ophthalmic Image Analysis (OPTIMA)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, retinal imaging, deep learning, convolutional neural networks, diabetic retinopathy screening, maculopathy, retinal disease diagnosis, automated disease detection, prognosis
- MeSH terms
- Deep Learning, Artificial Intelligence, Diagnostic Techniques, Ophthalmological, Humans, Nerve Net, Retinal Diseases, Reproducibility of Results, Neural Networks, Computer
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