AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis

TL;DR

This paper introduces an AI-driven computational framework that rapidly and accurately models patient-specific aortic stenosis hemodynamics from CT scans, facilitating improved diagnosis and personalized treatment planning.

Contribution

The study presents automated meshing algorithms and integrated modeling techniques that significantly accelerate and enhance the accuracy of personalized AS hemodynamics simulations.

Findings

Automated meshing is 100 times faster and more accurate than existing methods.

The framework accurately reproduces clinical hemodynamic measurements across diverse AS patients.

The approach enables personalized modeling for improved diagnosis and treatment planning.

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. 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.