Fundus diseases are major causes of visual impairment and blindness worldwide, especially in underdeveloped regions, where the shortage of ophthalmologists hinders timely diagnosis. AI-assisted fundus image analysis has several advantages, such as high accuracy, reduced workload, and improved accessibility, but it requires a large amount of expert-annotated data to build reliable models. To address this dilemma, we propose a general self-supervised machine learning framework that can handle diverse fundus diseases from unlabeled fundus images. Our method's AUC surpasses existing supervised approaches by 15.7%, and even exceeds performance of a single human expert. Furthermore, our model adapts well to various datasets from different regions, races, and heterogeneous image sources or qualities from multiple cameras or devices. Our method offers a label-free general framework to diagnose fundus diseases, which could potentially benefit telehealth programs for early screening of people at risk of vision loss.

翻译：眼底疾病是全球范围内导致视觉损伤和失明的主要原因，尤其在欠发达地区，眼科医生的短缺严重阻碍了及时诊断。人工智能辅助的眼底图像分析具有高准确性、降低工作负担和提高可及性等优势，但构建可靠模型需要大量专家标注数据。为解决这一困境，我们提出了一种通用的自监督机器学习框架，能够从无标签眼底图像中处理多种眼底疾病。该方法AUC指标超越现有监督方法达15.7%，甚至超过单个人类专家的表现。此外，我们的模型能良好适配来自不同地区、种族、异构图像来源或多摄像头/设备质量差异的各类数据集。该方法提供了一种免标注的通用框架用于诊断眼底疾病，有望为视力丧失风险人群的早期筛查远程医疗项目带来显著效益。