Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4432-4435. doi: 10.1109/EMBC46164.2021.9631082.
Coronary bifurcation lesions are a leading cause of Coronary Artery Disease (CAD). Despite its prevalence, coronary bifurcation lesions remain difficult to treat due to our incomplete understanding of how various features of lesion anatomy synergistically disrupt normal hemodynamic flow. In this work, we employ an interpretable machine learning algorithm, the Classification and Regression Tree (CART), to model the impact of these geometric features on local hemodynamic quantities. We generate a synthetic arterial database via computational fluid dynamic simulations and apply the CART approach to predict the time averaged wall shear stress (TAWSS) at two different locations within the cardiac vasculature. Our experimental results show that CART can estimate a simple, interpretable, yet accurately predictive nonlinear model of TAWSS as a function of such features.Clinical relevance- The fitted tree models have the potential to refine predictions of disturbed hemodynamic flow based on an individual's cardiac and lesion anatomy and consequently makes progress towards personalized treatment planning for CAD patients.
冠状动脉分叉病变是冠心病（CAD）的主要原因。尽管冠状动脉分叉病变很常见，但由于我们对病变解剖结构的各种特征如何协同破坏正常血流动力学的了解还不完整，因此仍然难以治疗。在这项工作中，我们采用了一种可解释的机器学习算法，分类回归树（CART），来模拟这些几何特征对局部血流动力学参数的影响。我们通过计算流体动力学模拟生成了一个合成动脉数据库，并应用 CART 方法来预测心脏血管内两个不同位置的时间平均壁切应力（TAWSS）。我们的实验结果表明，CART 可以估计 TAWSS 的一个简单、可解释但准确的非线性模型，作为这些特征的函数。临床相关性-拟合树模型有可能根据个体的心脏和病变解剖结构来改进对血流紊乱的预测，从而朝着 CAD 患者的个体化治疗计划取得进展。
