Soleimani M, Dashtbozorg B, Mirkhalaf M, Mirkhalaf S M
Institute of Continuum Mechanics, Leibniz Universität Hannover, Hannover, Germany.
Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
Heliyon. 2023 Jul 7;9(7):e17902. doi: 10.1016/j.heliyon.2023.e17902. eCollection 2023 Jul.
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.
动脉粥样硬化是一种涉及动脉壁硬化和/或增厚的医学病症。已经开发了数学多物理模型来预测不同条件下动脉粥样硬化的发展。然而，这些模型通常计算成本很高。在本研究中，我们使用机器学习技术，特别是人工神经网络（ANN），来提高这些模型的计算效率。创建了一个多物理有限元方法（FEM）模拟数据库，并用于训练和验证ANN模型。该模型能够快速准确地预测动脉粥样硬化的发展。与原始的FEM模拟相比，使用ANN模型获得了显著的计算增益。
