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<p>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.</p>

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Time and person sensitive foundation model for disease prediction and risk stratification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月14日

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  • 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

摘要

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.