AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.

ABSTRACT: Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High-fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use is currently limited by complex workflows necessitating lengthy expert-driven manual operations. Here, we propose an AI-powered computational framework for accelerated and democratized patient-specific modeling of AS hemodynamics from computed tomography (CT). First, we demonstrate that the automated meshing algorithms can generate task-ready geometries for both computational and benchtop simulations with higher accuracy and 100 times faster than existing approaches. Then, we show that the approach can be integrated with fluid-structure intera

SUBMITTER: Ozturk C 

PROVIDER: S-EPMC11791996 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature