  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. First,
we demonstrate that our 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 our approach
can be integrated with fluid-structure interaction and soft robotics models to
accurately recapitulate a broad spectrum of clinical hemodynamic measurements
of diverse AS patients. The efficiency and reliability of these algorithms make
them an ideal complementary tool for personalized high-fidelity modeling of AS
biomechanics, hemodynamics, and treatment planning.
