ArticleInternational journal of retina and vitreous2022
Diabetic retinopathy classification for supervised machine learning algorithms.
Article in International journal of retina and vitreous, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- An evening chronotype is associated with the incidence and progression of diabetic retinopathy in people with type 2 diabetes mellitus: a cohort study.Diabetologia · 2026Observational
- Interpreting Deep Neural Networks in Diabetic Retinopathy Grading: A Comparison with Human Decision Criteria.Life (Basel, Switzerland) · 2025Article
- A Beginner's Guide to Artificial Intelligence for Ophthalmologists.Ophthalmology and therapy · 2024Review
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- Developing and evaluating the usability of a web-based Diabetic Retinopathy Health Education Program for patients with type 2 diabetes mellitus.Saudi medical journal · 2023Article
- Impact of sustained hypertension on new cardiovascular events in patients with type 2 diabetes: KAMOGAWA-HBP study.Journal of clinical hypertension (Greenwich, Conn.) · 2022Article
- Development and Validation of a Deep Learning System for the Provision of a District-Wide Diabetes Retinal Screening Service.Clinical & experimental ophthalmologyArticle
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence and automated technology were first reported more than 70 years ago and nowadays provide unprecedented diagnostic accuracy, screening capacity, risk stratification, and workflow optimization. Diabetic retinopathy is an important cause of preventable blindness worldwide, and artificial intelligence technology provides precocious diagnosis, monitoring, and guide treatment. High-quality exams are fundamental in supervised artificial intelligence algorithms, but the lack of ground truth standards in retinal exams datasets is a problem. MAIN BODY: In this article, ETDRS, NHS, ICDR, SDGS diabetic retinopathy grading, and manual annotation are described and compared in publicly available datasets. The various DR labeling systems generate a fundamental problem for AI datasets. Possible solutions are standardization of DR classification and direct retinal-finding identifications.
conclusionReliable labeling methods also need to be considered in datasets with more trustworthy labeling.
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