Dong Pengfei, Ye Guochang, Kaya Mehmet, Gu Linxia
Department of Biomedical and Chemical Engineering, Florida Institute of Technology, Melbourne 32901, Australia.
Appl Sci (Basel). 2020 Sep 1;10(17). doi: 10.3390/app10175820. Epub 2020 Aug 22.
In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion.
在这项工作中，我们将有限元（FE）方法和机器学习（ML）方法相结合，以预测钙化冠状动脉中的支架扩张情况。在基于光学相干断层扫描图像重建的患者特异性动脉模型中捕捉支架置入过程。经过有限元模拟后，提取支架置入前动脉模型中120个横截面各自的八个几何特征以及相应的支架置入后管腔面积，用于训练和测试机器学习模型。这项工作采用了线性回归模型和具有三种不同核（线性、多项式和径向基函数核）的支持向量回归（SVR）模型。进一步评估了八个特征中的两个子组，即拉伸特征和钙化特征的预测能力。还通过对八个相邻横截面的每个特征求平均值，研究了相邻横截面对预测准确性的影响。结果表明，在偏差方面，SVR模型比线性回归模型提供了更好的预测。此外，与仅使用钙化特征相比，基于机理理解纳入拉伸特征可以提供更好的预测。然而，在预测偏差和误差范围方面，相邻横截面和单个横截面之间没有统计学上的显著差异。这项工作中基于模拟的机器学习框架可以增强对钙化冠状动脉病变中支架置入的机理理解，也为精确预测支架扩张铺平了道路。
