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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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Additional file 2 of Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study
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FDR26: A Fundus Dataset for Diabetic Retinopathy
Lamti, S., Kerzazi, N., Sekhsoukh, R., & Guechati, A. (2026) · Data in Brief · doi:10.1016/j.dib.2026.113379
Advances in Multimodal Imaging and Artificial Intelligence for the Diagnosis and Management of Diabetic Retinopathy: From Biomarkers to Personalized Care
Menna, F., Meduri, A., Lupo, S., Luca, L. D., Baldascino, A., & Vingolo, E. M. (2026) · Medicina · doi:10.3390/medicina62112099

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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