AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis
Source abstract
Abstract
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 (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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cardiac Valve Diseases and Treatments · Aortic Disease and Treatment Approaches · Coronary Interventions and Diagnostics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139‐4307 USA
Bioengineering Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton SO17 1BJ UK
Institute for Life Sciences University of Southampton Southampton SO17 1BJ United Kingdom
Departments of Biomedical Engineering and Radiology & Biomedical Imaging Yale University New Haven CT 06510 USA
Health Sciences and Technology Program Harvard University – Massachusetts Institute of Technology Cambridge MA 02139 USA
Department of Bioengineering Stanford University Palo Alto CA 94305 United States
Cardiovascular Innovation Research Center and Department of Cardiovascular Medicine Heart, Vascular & Thoracic Institute Cleveland Clinic Cleveland OH 44195 USA
Department of Chemical and Biomedical Engineering Cleveland State University Cleveland OH 44115 USA
Interventional Cardiology Hartford Hospital Hartford CT 06106 USA
Department of Biomedical Engineering Case Western Reserve University and Lerner Research Institute Cleveland Clinic Cleveland OH 44116 United States
Department of Mechanical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA
Funding
National Science Foundation · Additional Ventures · National Heart, Lung, and Blood Institute
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