ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Transformative biomechanics and mechanobiology breakthroughs shaping the future of health and medicine.Innovation (Cambridge (Mass.)) · 2026Review
- Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance.Frontiers in cardiovascular medicine · 2026Review
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- Artificial Intelligence in Risk Stratification and Outcome Prediction for Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.Journal of personalized medicine · 2025Review
- AI-Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Development of a standardized ovine aortic stenosis model: a pathomimetic platform for TAVI evaluation and durability testing.Frontiers in cardiovascular medicine · 2025Article
- Wearable and Implantable Soft Robots.Chemical reviews · 2024Review
- Robust automated calcification meshing for personalized cardiovascular biomechanics.NPJ digital medicine · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.