NPJ digital medicine · 2026
Time and person sensitive foundation model for disease prediction and risk stratification.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Wang Zheyuan, Zhou Yukun, Wu Yilan, Goh Jocelyn Hui Lin, Zou Ke, Guan Zhouyu, Chen Yibing, Yang Gabriel Dawei, Zhang Ping, Yin Changchang, Ran An Ran, Chee Miao Li, Xue Can Can, Soh Zhi da, Yew Samantha, Fang Danqi, Liu Xujia, Thinggaard Benjamin Sommer, Grauslund Jakob, Li Haoxuan, Jin Yixiao, Shu Jia, Li Tingyao, Jiang Nan, Chen Tingli, Li Huating, Wang Xiangning, Wu Qiang, Sabanayagam Charumathi, Wagner Siegfried K, Cheung Carol Y, Cheng Ching-Yu, Sheng Bin, Wong Tien Yin, Keane Pearse A, Tham Yih-Chung
Аннотация
Foundation models (FMs) enable generalizable medical AI, but existing retinal FMs perform best on cross-sectional classification and detection and are less effective for predicting disease incidence and progression. We present RETFound Plus, a CFP-based FM trained with temporal modeling on 1,304,292 fundus photographs from 304,345 participants across multiple visits to learn progression-aware representations. Compared with RETFound, RETFound Plus improved calibration and 5-year risk prediction across systemic and ocular diseases, with larger gains for systemic outcomes (stroke, myocardial infarction, diabetes and hypertension; +4-10% c-index) than ocular outcomes (diabetic retinopathy and glaucoma; +3-7% c-index), and improved risk stratification for systemic diseases (1.2-2.1-fold higher hazard-ratio trend). Results were consistent across external multi-regional, multi-ethnic datasets from the UK, US, Singapore, Hong Kong, and Denmark.