IEEE J Biomed Health Inform. 2018 Mar;22(2):537-544. doi: 10.1109/JBHI.2016.2639818. Epub 2016 Dec 14.
Computer simulations based on the finite element method represent powerful tools for modeling blood flow through arteries. However, due to its computational complexity, this approach may be inappropriate when results are needed quickly. In order to reduce computational time, in this paper, we proposed an alternative machine learning based approach for calculation of wall shear stress (WSS) distribution, which may play an important role in mechanisms related to initiation and development of atherosclerosis. In order to capture relationships between geometric parameters, blood density, dynamic viscosity and velocity, and WSS distribution of geometrically parameterized abdominal aortic aneurysm (AAA) and carotid bifurcation models, we proposed multivariate linear regression, multilayer perceptron neural network and Gaussian conditional random fields (GCRF). Results obtained in this paper show that machine learning approaches can successfully predict WSS distribution at different cardiac cycle time points. Even though all proposed methods showed high potential for WSS prediction, GCRF achieved the highest coefficient of determination (0.930-0.948 for AAA model and 0.946-0.954 for carotid bifurcation model) demonstrating benefits of accounting for spatial correlation. The proposed approach can be used as an alternative method for real time calculation of WSS distribution.
基于有限元法的计算机模拟是模拟动脉血流的强大工具。然而，由于其计算复杂性，当需要快速结果时，这种方法可能不太合适。为了减少计算时间，本文提出了一种替代的基于机器学习的方法来计算壁面切应力（WSS）分布，这可能在与动脉粥样硬化的发生和发展相关的机制中发挥重要作用。为了捕捉几何参数、血液密度、动态粘度和速度与几何参数化腹主动脉瘤（AAA）和颈动脉分叉模型的 WSS 分布之间的关系，我们提出了多元线性回归、多层感知器神经网络和高斯条件随机场（GCRF）。本文的结果表明，机器学习方法可以成功预测不同心动周期时间点的 WSS 分布。尽管所有提出的方法都显示出对 WSS 预测的高度潜力，但 GCRF 达到了最高的确定系数（AAA 模型为 0.930-0.948，颈动脉分叉模型为 0.946-0.954），证明了考虑空间相关性的好处。该方法可作为实时计算 WSS 分布的替代方法。
