Soleimani M, Dashtbozorg B, Mirkhalaf M, Mirkhalaf S. A multiphysics-based artificial neural networks model for atherosclerosis.
Heliyon 2023;
9:e17902. [PMID:
37483801 PMCID:
PMC10362161 DOI:
10.1016/j.heliyon.2023.e17902]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/25/2023] Open
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.
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