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 disorders 1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2. 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.
Original language | English |
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Pages (from-to) | 156-163 |
Number of pages | 8 |
Journal | Nature |
Volume | 622 |
Issue number | 7981 |
Early online date | 13 Sept 2023 |
DOIs | |
Publication status | Published - 05 Oct 2023 |
Bibliographical note
Funding Information:We thank P. Rawlinson for project management, C. Green and L. Wickham for information governance expertise, and A. Wenban, S. St John-Green and M. Barnfield for information technology support. This work is supported by Engineering and Physical Sciences Research Council grant nos. EP/M020533/1, EP/R014019/1 and EP/V034537/1, as well as the NIHR UCLH Biomedical Research Centre. S.K.W. is supported by a Medical Research Council Clinical Research Training Fellowship (grant no. MR/TR000953/1). P.A.K. is supported by a Moorfields Eye Charity Career Development Award (grant no. R190028A) and a UK Research & Innovation Future Leaders Fellowship (grant no. MR/T019050/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Publisher Copyright:
© 2023, The Author(s).
ASJC Scopus subject areas
- General