Artificial Intelligence-Based Risk Prediction of Coronary Ostial Obstruction After Transcatheter Aortic Valve Replacement in Bicuspid Population

This study focuses on the high incidence of coronary ostia obstruction (CO) after transcatheter aortic valve replacement (TAVR) in patients with bicuspid aortic valve (BAV). Due to the complex anatomical morphology of the aortic root in BAV patients and the lack of sufficient clinical data, preoperative CO risk assessment remains challenging. We propose an artificial intelligence (AI)-based parametric modeling simulation analysis workflow to reveal the relationship between the morphology of the aortic root and the high incidence of CO, clarify the underlying biomechanical mechanisms, and establish a risk assessment model. This study will not only provide a scientific basis for preoperative risk assessment in BAV patients, but also play a crucial role in improving the TAVR preoperative risk assessment system and advancing precision medicine for related diseases.