AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis
<|reference_start|>AI-powered multimodal modeling of personalized hemodynamics in aortic stenosis: 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.<|reference_end|>
@article{ozturk2024ai-powered,
    title={AI-powered multimodal modeling of personalized hemodynamics in aortic
  stenosis},
    author={Caglar Ozturk, Daniel H. Pak, Luca Rosalia, Debkalpa Goswami, Mary E.
  Robakowski, Raymond McKay, Christopher T. Nguyen, James S. Duncan, Ellen T.
  Roche},
    journal={arXiv preprint arXiv:2407.00535},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00535},
    primaryClass={cs.CE cs.CV}
}