Working toward the future of transcatheter aortic valve replacement: integrating computational modeling with patient-specific clinical care

Abstract

ABSTRACT Introduction Transcatheter aortic valve replacement (TAVR) is rapidly evolving to become the standard of care for aortic stenosis (AS) patients, as opposed to open-heart surgery. The emergence of new commercial TAVR devices, as well as technological advancements in AS diagnosis and procedural planning, combat post-TAVR complications but pose new challenges with patient selection and optimal timing of valve replacement. Computational modeling is a powerful tool that may help streamline solutions for heart teams facing these complexities. Areas covered This narrative review outlines the evolving landscape of TAVR patients – with a primary focus on AS – and examines the paradigm shift toward computational modeling in diagnostics, procedural planning, and intraoperative guidance. Mechanistic and AI-accelerated modeling studies were identified via PubMed, with publication dates ranging from January 2016-April 2026, while additional sources were identified without a publication date constraint. Expert opinion TAVR indications and available devices will continue to increase. Improved device durability is increasingly necessary as the mean age of TAVR patients decreases and life expectancy increases. Emerging computational techniques, such as radiomics and AI/ML tools to enhance standard imaging modalities, hold promise in identifying who and when to treat for AS or AR. Digital twins may facilitate TAVR planning and optimal treatment pathways for individual patients.