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ORIGINAL RESEARCH article
Front. Bioeng. Biotechnol.
Sec. Biomechanics
Volume 12 - 2024 |
doi: 10.3389/fbioe.2024.1360330
This article is part of the Research Topic Novel computational fluid dynamics methods for diagnosis, monitoring, prediction, and personalized treatment for cardiovascular disease and cancer metastasis View all 7 articles
Rapid Prediction of Wall Shear Stress in Stenosed Coronary Arteries based on Deep Learning
Provisionally accepted- 1 University College London, London, United Kingdom
- 2 Queen Mary University of London, London, United Kingdom
- 3 Leiden University, Leiden, Netherlands
- 4 Barts Health NHS Trust, London, England, United Kingdom
- 5 Centre for Medical Imaging, University College London, London, England, United Kingdom
There is increasing evidence that coronary artery wall shear stress (WSS) measurement provides useful prognostic information that allows prediction of adverse cardiovascular events. Computational Fluid Dynamics (CFD) has been extensively used in research to measure vessel physiology and examine the role of the local haemodynamic forces on the evolution of atherosclerosis. Nonetheless, CFD modelling remains computationally expensive and timeconsuming, making its direct use in clinical practice inconvenient. A number of studies have investigated the use of deep learning (DL) approaches for fast WSS prediction. However, in these reports, patient data were limited and most of them used synthetic data generation methods for developing the training set. In this paper, we implement 2 approaches for synthetic data generation and combine their output with real patient data in order to train a DL model with a U-net architecture for prediction of WSS in the coronary arteries. The model achieved 6.03% Normalised Mean Absolute Error (NMAE) with inference taking only 0.35 seconds; making this solution time-efficient and clinically relevant.
Keywords: deep learning, coronary artery, Stenosis, computational fluid dynamics, synthetic data
Received: 22 Dec 2023; Accepted: 12 Jul 2024.
Copyright: © 2024 Alamir, Tufaro, Trilli, Kitslaar, Mathur, Baumbach, Jacob, Bourantas and Torii. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Salwa H. Alamir, University College London, London, United Kingdom
Ryo Torii, University College London, London, United Kingdom
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Vincenzo Tufaro
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