Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic is commonly replaced with a bioprosthetic heart (BHV) via transcatheter replacement (TAVR) procedure. Accurate pre-operative planning crucial for successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid–solid interaction (FSI) offer solution that has been increasingly utilized to evaluate BHV mechanics dynamics. However, high costs complex operation modeling hinder its application. Recent advancements in deep learning (DL) domain can real-time surrogate render hemodynamic parameters few seconds, thus guiding clinicians select optimal treatment option. Herein, we provide comprehensive review classical approaches, medical imaging, DL approaches outcome assessment TAVR. Particularly, focus on previous studies, highlighting datasets, deployed models, achieved results. We emphasize critical challenges recommend several future directions innovative researchers tackle. Finally, an end-to-end smart framework outlined recommendation best design Ultimately, deploying such studies will support minimizing risks during therapy help improving patient care.