RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis.

ABSTRACT: Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, de

SUBMITTER: Zhang J 

PROVIDER: S-EPMC11063045 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature