Cai Li, Ren Lei, Wang Yongheng, Xie Wenxian, Zhu Guangyu, Gao Hao
Xi'an Key Laboratory of Scientific Computation and Applied Statistics, Northwestern Polytechnical University, Xi'an 710129, China.
NPU-UoG International Cooperative Lab for Computation and Application in Cardiology, Northwestern Polytechnical University, Xi'an 710129, China.
R Soc Open Sci. 2021 Jan 13;8(1):201121. doi: 10.1098/rsos.201121. eCollection 2021 Jan.
A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure-volume and pressure-strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure-volume and pressure-strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework.
生物力学研究前沿长期存在的一个问题是开发能够从临床数据中估计材料特性的快速方法。在本文中，我们研究了基于机器学习（ML）方法的三种替代模型，用于左心室（LV）心肌的快速参数估计。我们使用三种名为K近邻（KNN）、XGBoost和多层感知器（MLP）的ML方法来模拟舒张期充盈过程中压力与容积应变之间的关系。首先，为了训练替代模型，使用了LV舒张期充盈的正向有限元模拟器。然后将训练数据投影到低维参数化空间中。接下来，训练三个ML模型以学习压力-容积和压力-应变之间的关系。最后，利用这些训练好的替代模型构建一个逆参数估计问题。我们的结果表明，这三个ML模型能够很好地学习压力-容积和压力-应变之间的关系，并且使用替代模型进行参数推断可以在几分钟内完成。与KNN模型相比，XGBoost和MLP模型估计的参数不确定性要小得多。我们的结果进一步表明，与其他两个替代模型相比，XGBoost模型在预测LV舒张期动力学和估计被动参数方面表现更好。有必要进一步研究如何在多物理场和多尺度框架中使用XGBoost来模拟心脏泵功能。
