1. AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis
- Author
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Ozturk, Caglar, Pak, Daniel H., Rosalia, Luca, Goswami, Debkalpa, Robakowski, Mary E., McKay, Raymond, Nguyen, Christopher T., Duncan, James S., and Roche, Ellen T.
- Subjects
Computer Science - Computational Engineering, Finance, and Science ,Computer Science - Computer Vision and Pattern Recognition - 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., Comment: CO and DHP contributed equally to this work. JSD and ETR are corresponding authors
- Published
- 2024