A foundation model for generalizable disease detection from retinal images
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A foundation model for generalizable disease detection from retinal imagesData
2023-09-13Cita bibliográfica
Zhou, Y., Chia, M.A., Wagner, S.K. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023). https://doi.org/10.1038/s41586-023-06555-x
Resumo
[Absctract]: 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.
Palabras chave
Cardiovascular diseases
Medical imaging
Prognosis
Retinal diseases
Translational research
Medical imaging
Prognosis
Retinal diseases
Translational research
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
1476-4687
0028-0836
0028-0836