Key result
Deep learning models enable real-time hemodynamic predictions within seconds to guide TAVR planning.
Why the study?
High computational costs and complex operations hinder classical computational modeling for pre-operative TAVR planning, creating a need to review real-time deep learning alternatives.
Do deep learning approaches provide a faster and accurate alternative to classical computational modeling for TAVR planning and outcome assessment?
Do deep learning approaches provide a faster and accurate alternative to classical computational modeling for TAVR planning and outcome assessment?
Deep learning holds significant potential to replace time-consuming computational biomechanical models for real-time, patient-specific TAVR planning.
Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.
No takes yet. Share an insight, caveat, or question.
Tahir et al. (2023) conducted a review in Transcatheter aortic valve replacement (TAVR) planning and assessment. Deep learning approaches vs. Classical computational modeling (CFD, FEA, FSI) was evaluated on Performance and computational speed of deep learning vs classical computational modeling. Deep learning models offer a real-time surrogate to classical computational modeling capable of rendering hemodynamic parameters in a few seconds to guide clinicians during TAVR planning.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: