Retina (Philadelphia, Pa.) · 2026
Ultra-widefield color fundus images and artificial intelligence for diagnosis of diabetic retinopathy: A systematic review and meta-analysis.
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Hirosawa Kunihiko, Inomata Takenori, Nagino Ken, Sung Jaemyoung, Midorikawa-Inomata Akie, Eguchi Atsuko, Fujio Kenta, Kobayashi Hiroyuki, Nakao Shintaro
Аннотация
PURPOSE: Ultra-widefield (UWF) fundus cameras capture a larger retinal area without pupil dilation. We summarized evidence and diagnostic performance of artificial intelligence (AI)-driven diabetic retinopathy (DR) assessments using UWF images (UWFIs).
METHODS: We searched PubMed, Scopus, the Cochrane Library, and Web of Science to February 9, 2025, for studies evaluating DR using UWFIs and AI analyses. We followed the PRISMA guidelines and assessed study quality using the Joanna Briggs Institute Critical Appraisal Checklist for diagnostic accuracy. Forest plots and summary receiver operating characteristic curves estimated sensitivity and specificity of AI-driven DR screening using UWFI.
RESULTS: Of 527 records identified, 17 studies were included in the systematic review and four in the meta-analysis. All studies used Optos software. Among the included studies, two evaluated DR discrimination from multiple retinal disorders, six addressed DR stage classification based on guideline scales, six evaluated DR screening, and one each focused on referable retinal pathology detection, DR progression prediction, and detection of clinical DR features. The summary sensitivity, specificity, and area under receiver operating characteristic curve for AI-driven DR screening using UWFI were 85.0%, 72.5%, and 0.870 (0.838-0.897), respectively. UWFI-based AI screening showed acceptable sensitivity but limited specificity.
CONCLUSION: AI-driven DR screening using UWFIs may achieve acceptable sensitivity but with limited specificity; however, these findings should be interpreted with caution, given the limited number of available studies. Further studies using larger, diverse datasets with rigorous external validation across UWF platforms are needed to improve generalizability and clarify the clinical role of UWFI-based AI.
METHODS: We searched PubMed, Scopus, the Cochrane Library, and Web of Science to February 9, 2025, for studies evaluating DR using UWFIs and AI analyses. We followed the PRISMA guidelines and assessed study quality using the Joanna Briggs Institute Critical Appraisal Checklist for diagnostic accuracy. Forest plots and summary receiver operating characteristic curves estimated sensitivity and specificity of AI-driven DR screening using UWFI.
RESULTS: Of 527 records identified, 17 studies were included in the systematic review and four in the meta-analysis. All studies used Optos software. Among the included studies, two evaluated DR discrimination from multiple retinal disorders, six addressed DR stage classification based on guideline scales, six evaluated DR screening, and one each focused on referable retinal pathology detection, DR progression prediction, and detection of clinical DR features. The summary sensitivity, specificity, and area under receiver operating characteristic curve for AI-driven DR screening using UWFI were 85.0%, 72.5%, and 0.870 (0.838-0.897), respectively. UWFI-based AI screening showed acceptable sensitivity but limited specificity.
CONCLUSION: AI-driven DR screening using UWFIs may achieve acceptable sensitivity but with limited specificity; however, these findings should be interpreted with caution, given the limited number of available studies. Further studies using larger, diverse datasets with rigorous external validation across UWF platforms are needed to improve generalizability and clarify the clinical role of UWFI-based AI.