A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta

L Liang, W Mao, W Sun - Journal of biomechanics, 2020 - Elsevier
Numerical analysis methods including finite element analysis (FEA), computational fluid
dynamics (CFD), and fluid–structure interaction (FSI) analysis have been used to study the …

A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis

L Liang, M Liu, C Martin, W Sun - Journal of The Royal …, 2018 - royalsocietypublishing.org
Structural finite-element analysis (FEA) has been widely used to study the biomechanics of
human tissues and organs, as well as tissue–medical device interactions, and treatment …

Deep learning based centerline-aggregated aortic hemodynamics: An efficient alternative to numerical modeling of hemodynamics

P Yevtushenko, L Goubergrits… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Image-based patient-specific modelling of hemodynamics are gaining increased popularity
as a diagnosis and outcome prediction solution for a variety of cardiovascular diseases …

[HTML][HTML] Deep learning for computational hemodynamics: A brief review of recent advances

A Taebi - Fluids, 2022 - mdpi.com
Computational fluid dynamics (CFD) modeling of blood flow plays an important role in better
understanding various medical conditions, designing more effective drug delivery systems …

A review on computational fluid dynamics modelling in human thoracic aorta

AD Caballero, S Laín - Cardiovascular Engineering and Technology, 2013 - Springer
It has long been recognized that the forces and stresses produced by the blood flow on the
walls of the cardiovascular system are central to the development of different cardiovascular …

Deep learning-based surrogate model for three-dimensional patient-specific computational fluid dynamics

P Du, X Zhu, JX Wang - Physics of Fluids, 2022 - pubs.aip.org
Optimization and uncertainty quantification have been playing an increasingly important role
in computational hemodynamics. However, existing methods based on principled modeling …

[HTML][HTML] Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

G Li, H Wang, M Zhang, S Tupin, A Qiao, Y Liu… - Communications …, 2021 - nature.com
The clinical treatment planning of coronary heart disease requires hemodynamic
parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually …

[HTML][HTML] A deep learning framework for design and analysis of surgical bioprosthetic heart valves

A Balu, S Nallagonda, F Xu, A Krishnamurthy… - Scientific reports, 2019 - nature.com
Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they
are prone to fatigue failure; estimating their remaining life directly from medical images is …

Physics-informed neural networks (PINNs) for 4D hemodynamics prediction: An investigation of optimal framework based on vascular morphology

X Zhang, B Mao, Y Che, J Kang, M Luo, A Qiao… - Computers in Biology …, 2023 - Elsevier
Hemodynamic parameters are of great significance in the clinical diagnosis and treatment of
cardiovascular diseases. However, noninvasive, real-time and accurate acquisition of …

[HTML][HTML] Modeling of 3D blood flows with physics-informed neural networks: comparison of network architectures

P Moser, W Fenz, S Thumfart, I Ganitzer… - Fluids, 2023 - mdpi.com
Machine learning-based modeling of physical systems has attracted significant interest in
recent years. Based solely on the underlying physical equations and initial and boundary …