Individuals of the same chronological age differ in biological aging, and scalable, noninvasive markers are needed. The deep learning-derived retinal age gap (RAG) is a promising measure of retinal aging, but its associations with real-world health determinants remain unclear. We developed a multi-task model to predict...
Boa Jang, Richul Oh, Taehee Lee et al.· GeroScience· 0 citations
Background/Objectives: Double reading with arbitration improves diagnostic reliability in fundus screening but requires repeated human interpretation. Whether artificial intelligence (AI) can serve as a first-pass decision source within this workflow, and whether this applies consistently across retinal diseases with d...
Jihyeon Baek, Richul Oh, Doohyun Park et al.· Diagnostics· 0 citations
An AI-assisted negative-screening workflow for diabetic retinopathy, retinal vein occlusion, and age-related macular degeneration using 6,904 color fundus photographs independently graded by three retina specialists supports disease-specific implementation strategies that balance workload reduction against missed posit...
Doohyun Park, Richul Oh, Jihyeon Baek et al.· PLOS Digital Health· 0 citations
Variability in ultra-widefield (UWF) fundus image acquisition and the lack of standardized post-acquisition processing complicate longitudinal monitoring and quantitative analysis because of differences in retinal coverage, image orientation, and acquisition-related artifacts. This study developed a deep learning-based...
Richul Oh, Jeong Min Lee, Won Jung Lee et al.· Journal of imaging informati...· 0 citations
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