ArticleInternational journal of ophthalmology2023
Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.
Article in International journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Integrating Nursing-Teleophthalmology Improves Diabetic Retinopathy Screening in Primary Healthcare, Reducing Unnecessary Referrals to Specialist Healthcare.International journal of nursing practice · 2025Article
- Artificial intelligence support improves diagnosis accuracy in anterior segment eye diseases.Scientific reports · 2025Article
- Clinical performance of an interactive platform based on artificial intelligence in ophthalmology: experience in a third-level reference center.Frontiers in medicine · 2025Article
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
- Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach.PeerJ. Computer science · 2024Article
- The application of artificial intelligence in diabetic retinopathy: progress and prospects.Frontiers in cell and developmental biology · 2024Review
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Authors and funding
6 authors.
Funding
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Abstract
Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.
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