A foundation model for generalizable disease detection from retinal images
Zhou, Yukun Chia, Mark A Wagner, Siegfried K Ayhan, Murat S Williamson, Dominic J Struyven, Robbert R Liu, Timing Xu, Moucheng Lozano, Mateo G Woodward-Court, Peter
Zhou, Yukun
Chia, Mark A
Wagner, Siegfried K
Ayhan, Murat S
Williamson, Dominic J
Struyven, Robbert R
Liu, Timing
Xu, Moucheng
Lozano, Mateo G
Woodward-Court, Peter
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.
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Date
2023-09-13
Type
Article
Subject
Ophthalmology
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Citation
Zhou Y, Chia MA, Wagner SK, Ayhan MS, Williamson DJ, Struyven RR, Liu T, Xu M, Lozano MG, Woodward-Court P, Kihara Y; UK Biobank Eye & Vision Consortium; Altmann A, Lee AY, Topol EJ, Denniston AK, Alexander DC, Keane PA. A foundation model for generalizable disease detection from retinal images. Nature. 2023 Oct;622(7981):156-163. doi: 10.1038/s41586-023-06555-x. Epub 2023 Sep 13. PMID: 37704728; PMCID: PMC10550819.
Journal / Source Title
Nature
DOI
10.1038/s41586-023-06555-x
PMID
37704728
Publisher
Nature Publishing Group
Publisher’s URL
http://www.nature.com/nature