A multiphysics-based artificial neural networks model for atherosclerosis

authored by: M. Soleimani, B. Dashtbozorg, M. Mirkhalaf, S. M. Mirkhalaf
Abstract: Atherosclerosis is a medical condition involving the hardening and/or thickening of arteries' walls. Mathematical multi-physics models have been developed to predict the development of atherosclerosis under different conditions. However, these models are typically computationally expensive. In this study, we used machine learning techniques, particularly artificial neural networks (ANN), to enhance the computational efficiency of these models. A database of multi-physics Finite Element Method (FEM) simulations was created and used for training and validating an ANN model. The model is capable of quick and accurate prediction of atherosclerosis development. A remarkable computational gain is obtained using the ANN model compared to the original FEM simulations.
Organisation(s): Institute of Continuum Mechanics
External Organisation(s): Netherlands Cancer Institute
Queensland University of Technology
University of Gothenburg
Type: Article
Journal: Heliyon
Volume: 9
ISSN: 2405-8440
Publication date: 07.2023
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: General
Electronic version(s): https://doi.org/10.1016/j.heliyon.2023.e17902 (Access: Open)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{c7195555f987410d8f42cfc47a8c91e0,
title = "A multiphysics-based artificial neural networks model for atherosclerosis",
abstract = "Atherosclerosis is a medical condition involving the hardening and/or thickening of arteries' walls. Mathematical multi-physics models have been developed to predict the development of atherosclerosis under different conditions. However, these models are typically computationally expensive. In this study, we used machine learning techniques, particularly artificial neural networks (ANN), to enhance the computational efficiency of these models. A database of multi-physics Finite Element Method (FEM) simulations was created and used for training and validating an ANN model. The model is capable of quick and accurate prediction of atherosclerosis development. A remarkable computational gain is obtained using the ANN model compared to the original FEM simulations.",
keywords = "Artificial neural networks, Atherosclerosis, Finite Element Modeling, Multi-physics",
author = "M. Soleimani and B. Dashtbozorg and M. Mirkhalaf and Mirkhalaf, {S. M.}",
note = "Funding Information: Mohammad Mirkhalaf was supported by Australian Research Council [ DE210100975 ]. Funding Information: Meisam Soleimani was supported by Deutsche Forschungsgemeinschaft [ TRR 298 ]. ",
year = "2023",
month = jul,
doi = "10.1016/j.heliyon.2023.e17902",
language = "English",
volume = "9",
journal = "Heliyon",
issn = "2405-8440",
publisher = "Elsevier BV",
number = "7",
}