An ultrasound foundation model for the stratification of vision impairment and eye cancer risk.

Abstract

The prevalence of vision disorders and eye cancer risk, driven by global population aging, necessitates precise ophthalmological disease screening, yet standardized and robust approaches for risk stratification remain absent. Here, we present SonoEye, a vision-language ultrasound foundation model for eye disease risk stratification, pre-trained on 215,356 image-text pairs from 70,452 patients using contrastive learning and fine-tuned with consensus-confirmed patient-level labels. The model achieves 98.3% screening sensitivity and a mean accuracy of 96.3% in differentiating 18 diseases. We established the Eye Reporting and Data System (Eye-RADS), a 4-tier risk stratification framework (normal, low-vision risk, high-vision risk, and tumor risk), demonstrating excellent Cohen's kappa agreement across internal (0.808) and external (0.677-0.685) test cohorts. Notably, incorporating age significantly improved Eye-RADS performance in elderly but not younger populations, indicating age-related features in eye care. Through image-text alignment, SonoEye generates structured clinical reports with interpretable attention-based visualizations, advancing automated vision risk stratification, particularly for aging populations in resource-limited settings.