AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis
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 workflow...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-02-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202404755 |
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| author | Caglar Ozturk Daniel H. Pak Luca Rosalia Debkalpa Goswami Mary E. Robakowski Raymond McKay Christopher T. Nguyen James S. Duncan Ellen T. Roche |
| author_facet | Caglar Ozturk Daniel H. Pak Luca Rosalia Debkalpa Goswami Mary E. Robakowski Raymond McKay Christopher T. Nguyen James S. Duncan Ellen T. Roche |
| author_sort | Caglar Ozturk |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2a34c563b5ef4ad8ace5743feb98d79c |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-2a34c563b5ef4ad8ace5743feb98d79c2025-02-04T13:14:54ZengWileyAdvanced Science2198-38442025-02-01125n/an/a10.1002/advs.202404755AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic StenosisCaglar Ozturk0Daniel H. Pak1Luca Rosalia2Debkalpa Goswami3Mary E. Robakowski4Raymond McKay5Christopher T. Nguyen6James S. Duncan7Ellen T. Roche8Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139‐4307 USADepartments of Biomedical Engineering and Radiology & Biomedical Imaging Yale University New Haven CT 06510 USAInstitute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139‐4307 USACardiovascular Innovation Research Center and Department of Cardiovascular Medicine Heart, Vascular & Thoracic Institute Cleveland Clinic Cleveland OH 44195 USACardiovascular Innovation Research Center and Department of Cardiovascular Medicine Heart, Vascular & Thoracic Institute Cleveland Clinic Cleveland OH 44195 USAInterventional Cardiology Hartford Hospital Hartford CT 06106 USACardiovascular Innovation Research Center and Department of Cardiovascular Medicine Heart, Vascular & Thoracic Institute Cleveland Clinic Cleveland OH 44195 USADepartments of Biomedical Engineering and Radiology & Biomedical Imaging Yale University New Haven CT 06510 USAInstitute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge MA 02139‐4307 USAAbstract 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.https://doi.org/10.1002/advs.202404755aortic stenosiscomputational fluid dynamicsdeep learningfluid‐structure interactionheart meshingmultimodal modeling |
| spellingShingle | Caglar Ozturk Daniel H. Pak Luca Rosalia Debkalpa Goswami Mary E. Robakowski Raymond McKay Christopher T. Nguyen James S. Duncan Ellen T. Roche AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis Advanced Science aortic stenosis computational fluid dynamics deep learning fluid‐structure interaction heart meshing multimodal modeling |
| title | AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis |
| title_full | AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis |
| title_fullStr | AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis |
| title_full_unstemmed | AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis |
| title_short | AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis |
| title_sort | ai powered multimodal modeling of personalized hemodynamics in aortic stenosis |
| topic | aortic stenosis computational fluid dynamics deep learning fluid‐structure interaction heart meshing multimodal modeling |
| url | https://doi.org/10.1002/advs.202404755 |
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