A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
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 (CT). First, we demonstrate that the 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 the 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.
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 (CT). First, we demonstrate that the 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 the 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.
AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis
Ozturk, Caglar (author) / Pak, Daniel H. (author) / Rosalia, Luca (author) / Goswami, Debkalpa (author) / Robakowski, Mary E. (author) / McKay, Raymond (author) / Nguyen, Christopher T. (author) / Duncan, James S. (author) / Roche, Ellen T. (author)
Advanced Science ; 12
2025-02-01
16 pages
Article (Journal)
Electronic Resource
English
AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis
| Wiley | 2025
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| BASE | 2020