AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis
This work addresses the need for accelerated and democratized personalized modeling in aortic stenosis management, offering a tool for therapeutic innovation and tailored treatment planning, though it is incremental in automating existing workflows.
The researchers tackled the problem of complex, slow workflows for patient-specific modeling of aortic stenosis hemodynamics by developing an AI-powered computational framework that automates geometry generation and integrates with simulation models, achieving 100 times faster processing with higher accuracy and accurately recapitulating clinical hemodynamic measurements.
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.