ArticleFrontiers in physiology2025
Prediction of time averaged wall shear stress distribution in coronary arteries' bifurcation varying in morphological features via deep learning.
Article in Frontiers in physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mechanopriming by vascular stiffness and phenotypic reprogramming by disturbed flow: mechanobiology and clinical translation in atherosclerosis.Frontiers in cardiovascular medicine · 2026Pooled it
- From Geometry to Flow Allocation: A Physics-Based Framework for Interpretable Microvascular Hemodynamics.Bioengineering (Basel, Switzerland) · 2026Article
- Image-Based CFD Analysis of Blood Flow in Non-Porous and Porous Prosthetic Vascular Grafts.Bioengineering (Basel, Switzerland) · 2026Article
- Hemodynamic Effects of Aortic Stenosis on Coronary Flow Dynamics.Annals of biomedical engineering · 2026Article
- Article
- Numerical and Experimental Investigation of Mixing Enhancement in a Zigzag Passive Micromixer with D-Shaped Obstacles.Micromachines · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Introduction: Understanding the hemodynamics of blood circulation is crucial to reveal the processes contributing to stenosis and atherosclerosis development. Method: Computational fluid dynamics (CFD) facilitates this understanding by simulating blood flow patterns in coronary arteries. Nevertheless, applying CFD in fast-response scenarios presents challenge due to the high computational costs. To overcome this challenge, we integrate a deep learning (DL) method to improve efficiency and responsiveness. This study presents a DL approach for predicting Time-Averaged Wall Shear Stress (TAWSS) values in coronary arteries' bifurcation. Results: To prepare the dataset, 1800 idealized models with varying morphological parameters are created. Afterward, we design a CNN-based U-net architecture to predict TAWSS by the point cloud of the geometries. Moreover, this architecture is implemented using TensorFlow 2.3.0. Our results indicate that the proposed algorithms can generate results in less than one second, showcasing their suitability for applications in terms of computational efficiency. Discussion: Furthermore, the DL-based predictions demonstrate strong agreement with results from CFD simulations, with a normalized mean absolute error of only 2.53% across various cases.
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