Time and person sensitive foundation model for disease prediction and risk stratification.
retrospective_cohort · Level III
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- Record sourced from PubMed, PMID 41832356.
- Also identified by DOI 10.1038/s41746-026-02524-6.
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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.