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A foundation model for generalizable disease detection from retinal images

Author

Listed:
  • Yukun Zhou

    (University College London
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Mark A. Chia

    (NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Siegfried K. Wagner

    (NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Murat S. Ayhan

    (University College London
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Dominic J. Williamson

    (University College London
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Robbert R. Struyven

    (University College London
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Timing Liu

    (NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust)

  • Moucheng Xu

    (University College London
    University College London)

  • Mateo G. Lozano

    (NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University of Coruña)

  • Peter Woodward-Court

    (University College London
    NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

  • Yuka Kihara

    (University of Washington
    University of Washington)

  • Andre Altmann

    (University College London
    University College London)

  • Aaron Y. Lee

    (University of Washington
    University of Washington)

  • Eric J. Topol

    (Scripps Research)

  • Alastair K. Denniston

    (University of Birmingham
    University Hospitals Birmingham NHS Foundation Trust)

  • Daniel C. Alexander

    (University College London
    University College London)

  • Pearse A. Keane

    (NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust
    University College London)

Abstract

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.

Suggested Citation

  • Yukun Zhou & Mark A. Chia & Siegfried K. Wagner & Murat S. Ayhan & Dominic J. Williamson & Robbert R. Struyven & Timing Liu & Moucheng Xu & Mateo G. Lozano & Peter Woodward-Court & Yuka Kihara & Andre, 2023. "A foundation model for generalizable disease detection from retinal images," Nature, Nature, vol. 622(7981), pages 156-163, October.
  • Handle: RePEc:nat:nature:v:622:y:2023:i:7981:d:10.1038_s41586-023-06555-x
    DOI: 10.1038/s41586-023-06555-x
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    Cited by:

    1. Thiers, Fabio A. & Lucy, Kimberly, 2024. "A Distinct Approach to Clinical GenAI Oversight," OSF Preprints vm6zy, Center for Open Science.
    2. Weijian Huang & Cheng Li & Hong-Yu Zhou & Hao Yang & Jiarun Liu & Yong Liang & Hairong Zheng & Shaoting Zhang & Shanshan Wang, 2024. "Enhancing representation in radiography-reports foundation model: a granular alignment algorithm using masked contrastive learning," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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