Details
| Original language | English |
|---|---|
| Article number | e17902 |
| Journal | Heliyon |
| Volume | 9 |
| Issue number | 7 |
| Early online date | 7 Jul 2023 |
| Publication status | Published - Jul 2023 |
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
ASJC Scopus subject areas
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In: Heliyon, Vol. 9, No. 7, e17902, 07.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A multiphysics-based artificial neural networks model for atherosclerosis
AU - Soleimani, M.
AU - Dashtbozorg, B.
AU - Mirkhalaf, M.
AU - Mirkhalaf, S. M.
N1 - Funding Information: Mohammad Mirkhalaf was supported by Australian Research Council [ DE210100975 ]. Funding Information: Meisam Soleimani was supported by Deutsche Forschungsgemeinschaft [ TRR 298 ].
PY - 2023/7
Y1 - 2023/7
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Atherosclerosis
KW - Finite Element Modeling
KW - Multi-physics
UR - http://www.scopus.com/inward/record.url?scp=85164386891&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2023.e17902
DO - 10.1016/j.heliyon.2023.e17902
M3 - Article
AN - SCOPUS:85164386891
VL - 9
JO - Heliyon
JF - Heliyon
SN - 2405-8440
IS - 7
M1 - e17902
ER -