ArticleMedical & biological engineering & computing2025
Rapid wall shear stress prediction for aortic aneurysms using deep learning: a fast alternative to CFD.
Article in Medical & biological engineering & computing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Integrated Assessment of Wall Shear Stress-Related Hemodynamic Parameters in Abdominal Aortic Aneurysms: A Retrospective Cross-Sectional Study on Ruptured Cases.International journal for numerical methods in biomedical engineering · 2026Article
- Physics-Informed Neural Networks Meet Multimodal Large Language Models: Biomechanical Simulation in Aortic Aneurysm.Cyborg and bionic systems (Washington, D.C.) · 2026Article
- Article
- From Heart to Abdominal Aorta: Integrating Multi-Modal Cardiac Imaging Derived Haemodynamic Biomarkers for Abdominal Aortic Aneurysm Risk Stratification, Surveillance, Pre-Operative Assessment and Therapeutic Decision-Making.Diagnostics (Basel, Switzerland) · 2025Review
- Integrated multidisciplinary approach to aneurysm hemodynamic analysis: numerical simulation,Frontiers in bioengineering and biotechnology · 2025Review
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Authors and funding
11 authors.
Funding
Abstract
Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment. Computational fluid dynamics (CFD) is a powerful approach for WSS calculations, but they are computationally intensive, hindering time-sensitive clinical decision-making. In this study, we propose a deep learning (DL) surrogate, MultiViewUNet, to rapidly predict time-averaged WSS (TAWSS) distributions on abdominal aortic aneurysms (AAA). Our novel approach employs a domain transformation technique to translate complex aortic geometries into representations compatible with state-of-the-art neural networks. MultiViewUNet was trained on
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Registered trials
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