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
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Thiers, Fabio A. & Lucy, Kimberly, 2024.
"A Distinct Approach to Clinical GenAI Oversight,"
OSF Preprints
vm6zy, Center for Open Science.
- 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.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:622:y:2023:i:7981:d:10.1038_s41586-023-06555-x. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.