Changchang Yin

Abstract Surgical site infection (SSI) is one of the most common and costly healthcare-associated infections, and surgical wound care remains a significant challenge for preventing SSIs and improving patient outcomes. Although deep learning has been explored for preliminary surgical wound screening, progress is limited by data privacy concerns and the high cost of expert annotation.

Abstract Foundation models (FMs) enable generalizable medical AI, but existing retinal FMs perform best on cross-sectional classification and detection and are less effective for predicting disease incidence and progression. We present RETFound Plus, a CFP-based FM trained with temporal modeling on 1,304,292 fundus photographs from 304,345 participants across multiple visits to learn progression-aware representations.

1. Introduction With the rapid development of the economy, car ownership has gradually increased, resulting in problems such as traffic congestion and environmental pollution. As an effective method to solve these problems, shared transportation schemes with the advantages of efficient car utilization and reducing car ownership and carbon dioxide emission has been widely implemented around the world [,].