  Previous foundation models for retinal images were pre-trained with limited
disease categories and knowledge base. Here we introduce RetiZero, a
vision-language foundation model that leverages knowledge from over 400 fundus
diseases. To RetiZero's pre-training, we compiled 341,896 fundus images paired
with text descriptions, sourced from public datasets, ophthalmic literature,
and online resources, encompassing a diverse range of diseases across multiple
ethnicities and countries. RetiZero exhibits superior performance in several
downstream tasks, including zero-shot disease recognition, image-to-image
retrieval, and internal- and cross-domain disease identification. In zero-shot
scenarios, RetiZero achieves Top5 accuracy scores of 0.8430 for 15 fundus
diseases and 0.7561 for 52 fundus diseases. For image retrieval, it achieves
Top5 scores of 0.9500 and 0.8860 for the same disease sets, respectively.
Clinical evaluations show that RetiZero's Top3 zero-shot performance surpasses
the average of 19 ophthalmologists from Singapore, China and the United States.
Furthermore, RetiZero significantly enhances clinicians' accuracy in diagnosing
fundus disease. These findings underscore the value of integrating the RetiZero
foundation model into clinical settings, where a variety of fundus diseases are
encountered.
