Year: 2026
Type: article
Abstract: 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.
Source: npj Digital Medicine
Authors: Zheyuan Wang, Yukun Zhou, Yilan Wu, Jocelyn Hui Lin Goh, Ke Zou, Zhouyu Guan, Yibing Chen, Gabriel Dawei Yang, Ping Zhang, Changchang Yin, An Ran Ran, Miao Li Chee, Can can Xue, Zhi da Soh, Samantha Yew, Danqi Fang, Xujia Liu, Benjamin Sommer Thinggaard, Jakob Grauslund, Haoxuan Li, Yixiao Jin, Jia Shu, Tingyao Li, Nan Jiang, Tingli Chen, Huating Li, Xiangning Wang, Qiang Wu, Charumathi Sabanayagam, Siegfried K. Wagner, Carol Y. Cheung, Ching-Yu Cheng, Bin Sheng, Tien Yin Wong, Pearse A. Keane, Yih-Chung Tham
Institutions: Shanghai Jiao Tong University, Moorfields Eye Hospital NHS Foundation Trust, NIHR Moorfields Biomedical Research Centre, University College London, Beijing Tsinghua Chang Gung Hospital, Tsinghua University, National University of Singapore, Singapore National Eye Center, Singapore Eye Research Institute, Shanghai Sixth People's Hospital, The Ohio State University, Chinese University of Hong Kong, Odense University Hospital, University of Southern Denmark, Shanghai University of Sport
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Funders: Shanghai Municipal Health Commission, National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality, Moorfields Eye Charity, National Science and Technology Major Project, Medical Research Council, National Medical Research Council, National Key Research and Development Program of China, Alcon Research Institute
OpenAlex ID: W7135406865