Diagnosis of Multiple Fundus Disorders Amidst a Scarcity of Medical Experts via Self-Supervised Machine Learning

Abstract

Fundus diseases are prevalent causes of visual impairment and blindness worldwide, particularly in regions with limited access to ophthalmologists for timely diagnosis. Current approaches to fundus disease diagnosis heavily rely on expert-annotated data and AI-assisted image analysis, offering advantages, such as improved accuracy and accessibility. However, the dependency on annotated data poses a significant challenge, especially in regions with limited resources. To address this challenge, we propose a label-free general framework based on self-supervised machine learning. We performed feature distillation on a large number of unlabeled fundus images and employed a linear classifier for the detection of different fundus diseases. In validation experiments on the public and external validation fundus data sets, our model surpassed existing supervised approaches, achieving a remarkable increase in the area under the curve (AUC) of 15.7%, and even outperformed individual human experts. Our approach offers a promising solution to the limitations of current diagnostic methods, enhancing the potential for early and accurate detection of fundus diseases in resource-constrained settings.