ArticleClinical ophthalmology (Auckland, N.Z.)2025
AI-Assisted Screening for Diabetic Retinopathy and Fundus Abnormalities in a Large-Scale Physical Examination Population.
Article in Clinical ophthalmology (Auckland, N.Z.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Ocular Manifestations of Metabolic Syndrome: A Systemic Vascular Phenotype.Diabetes, obesity & metabolism · 2026Review
- Validation of the Eyerobo FC Portable Fundus Camera for Diabetic Retinopathy Screening Using Public Datasets and Deep Learning.Ophthalmology and therapy · 2026Article
- Prediction of carotid vulnerable plaques in patients with type 2 diabetes using interpretable machine learning models.Frontiers in endocrinology · 2026Article
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Authors and funding
4 authors.
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Abstract
Purpose: Due to the high incidence rate of eye diseases, various artificial intelligence (AI) screening systems for retinal eye disorders have been developed at present. This study aimed to evaluate the diagnostic performance and clinical value of an AI-assisted system for large-scale screening of diabetic retinopathy (DR) and other fundus abnormalities in a real-world physical examination population. Methods: This retrospective study analyzed 54,353 fundus examination records collected from the local hospital in 2020. An AI-assisted system was used to screen for DR and other retinal abnormalities. Manual interpretation was conducted to validate AI predictions, and data were stratified by comorbidities and systemic risk factors. Results: Approximately 25% of individuals tested positive for fundus lesions. The AI-assisted system demonstrated high diagnostic performance, with a negative predictive value ≥96% and a positive predictive value ≥90%. Common abnormalities detected included retinal vascular sclerosis, drusen, maculopathy, optic cup enlargement, and hemorrhage. Higher positive detection rates were observed in individuals with a history of diabetes, hypertension, high myopia, and other systemic conditions, with detection rates increasing with disease duration. Conclusion: AI-assisted screening offers an effective, scalable approach for early DR detection and can also identify systemic diseases with retinal manifestations. Integration of AI with big data platforms enables timely intervention, especially in underserved areas. Building a multi-institutional DR data platform may revolutionize retinal disease management and improve patient outcomes. This study supports the clinical application of AI in enhancing diagnostic efficiency and targeting high-risk populations for early intervention.
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