小柯机器人

英国伦敦大学学院Pearse A. Keane等研究人员合作建立从视网膜图像检测疾病的基础模型。这一研究成果于2023年9月13日在线发表在国际学术期刊《自然》上。

研究人员介绍一种视网膜图像基础模型RETFound，它能从无标签的视网膜图像中学习可通用的表征，并为多个应用中的标签高效模型适配提供基础。具体来说，RETFound是通过自我监督学习的方式在160万张无标签视网膜图像上进行训练的，然后适应于带有明确标签的疾病检测任务。

研究结果表明，经过调整的RETFound在威胁视力的眼部疾病诊断和预后以及复杂系统疾病（如心力衰竭和心肌梗塞）的事件预测方面，始终优于几个比较模型，而且标记数据较少。RETFound为提高模型性能和减轻专家标注工作量提供了可推广的解决方案，从而使视网膜成像技术在临床AI领域得到广泛应用。

据了解，医学人工智能（AI）在识别视网膜图像中的健康状况迹象以及加快眼部疾病和全身性疾病诊断方面具有巨大潜力。然而，AI模型的开发需要大量注释，而且模型通常针对特定任务，对不同临床应用的通用性有限。

附：英文原文

Title: A foundation model for generalizable disease detection from retinal images

Author: 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.

Issue&Volume: 2023-09-13

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. RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled images, is trained on 1.6 million unlabelled images by self-supervised learning and then adapted to disease detection tasks with explicit labels.

DOI: 10.1038/s41586-023-06555-x

Source: https://www.nature.com/articles/s41586-023-06555-x

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html