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.

翻译：主动脉瓣狭窄（AS）是发达国家最常见的心脏瓣膜疾病。高保真度的临床前模型能够促进治疗创新、早期诊断和个体化治疗方案制定，从而改善AS的临床管理。然而，目前其应用受到复杂工作流程的限制，这些流程需要耗时冗长的专家手动操作。本文提出一种AI驱动的计算框架，用于基于计算机断层扫描实现快速化、普适化的AS患者特异性血流动力学建模。首先，我们证明所提出的自动网格生成算法能够为计算模拟和实验台模拟生成任务就绪的几何模型，其精度高于现有方法，且速度提升100倍。随后，我们展示了该方法可与流固耦合模型及软体机器人模型相结合，精确复现不同AS患者群体广泛的临床血流动力学测量指标。这些算法的高效性与可靠性，使其成为AS生物力学、血流动力学及治疗规划个性化高保真建模的理想辅助工具。