Details

Autor(en) / Beteiligte

• 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
• Kihara, Yuka
• Altmann, Andre
• Lee, Aaron Y
• Topol, Eric J
• Denniston, Alastair K
• Alexander, Daniel C
• Keane, Pearse A

Titel

A foundation model for generalizable disease detection from retinal images

Ist Teil von

• Nature (London), 2023-10, Vol.622 (7981), p.156-163

Ort / Verlag

England

Erscheinungsjahr

2023

Quelle

MEDLINE

Beschreibungen/Notizen

• 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 . However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications . 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.