AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis is a research paper published in Advanced Science (2024). On theSindex it has a DataRank of 0. It has been cited 6 times.
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National Science Foundation
Grant: 1847541
CAREER: Hybrid Biorobotic Matrices to Simulate Diaphragmatic and Myocardial Biomechanics
National Heart, Lung, and Blood Institute
Grant: F31HL162505
National Heart, Lung, and Blood Institute
Grant: R01HL121226
National Heart, Lung, and Blood Institute
Grant: R01HL142036
National Heart, Lung, and Blood Institute
Grant: T32HL098069
National Heart, Lung, and Blood Institute
Grant: R01EB033853
National Heart, Lung, and Blood Institute
Grant: R01HL151704
National Heart, Lung, and Blood Institute
Grant: R01HL159010
National Institutes of Health
Grant: 5T32HL098069-08
Training in Multi-modality Molecular and Translational Cardiovascular Imaging
National Institutes of Health
Grant: 5R01HL159010-04
Microstructural Response of the Myocardium to Mechanical Load
National Institutes of Health
Grant: 5F31HL162505-02
Data-Driven Automation of Patient-Specific Finite Element Modeling for TAVR
National Institutes of Health
Grant: 1R01EB033853-01A1
Modeling, measurement and prediction of cardiac magneto-stimulation thresholds
National Institutes of Health
Grant: 1R01HL121226-01
Integrated RF and B-mode Deformation Analysis for 4D Stress Echocardiography
National Institutes of Health
Grant: 1R01HL142036-01
A novel computing framework to automatically process cardiac valve image data and predict treatment outcomes
National Institutes of Health
Grant: 7R01HL151704-04
Serial evaluation of cardioprotective effects of exercise training in heart failure using cardiac diffusion tensor MRI
Additional Ventures
FWCI
2.05
Citation Percentile
0.9%
Citation Trend
Fields of Study
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Sustainable Development Goals