This retrospective multicenter cohort study aims to develop and validate an artificial intelligence model integrating electrocardiography (ECG) and chest radiography (CXR) to predict future progression of regurgitant valvular heart disease (rVHD), including aortic, mitral, and tricuspid regurgitation. Adult patients with ECG, CXR, and echocardiography obtained within 60 days, together with follow-up echocardiographic data, are included. The primary objective is to determine whether multimodal ECG+CXR modeling improves prediction of progression to moderate or severe regurgitation beyond ECG-only or CXR-only models. Secondary objectives include evaluation of clinical utility, risk stratification, and model interpretability. This study is intended to assess whether routinely acquired ECG and CXR can be used to support surveillance echocardiography and risk-directed management in patients at risk of future rVHD progression.