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.

生物力学研究前沿的一个长期存在的问题是开发能够根据临床数据估计材料特性的快速方法。在本文中，我们研究了三种基于机器学习（ML）方法的替代模型，用于左心室（LV）心肌的快速参数估计。我们使用三种称为 K 最近邻 (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和体积应变之间的关系。首先，为了训练替代模型，使用左心室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，训练三个机器学习模型来学习压力-体积和压力-应变的关系。最后，通过使用那些经过训练的代理模型来制定逆参数估计问题。我们的结果表明，三个机器学习模型可以很好地学习压力-体积和压力-应变的关系，并且使用代理模型的参数推断可以在几分钟内完成。与 KNN 模型相比，XGBoost 和 MLP 模型的估计参数的不确定性要小得多。我们的结果进一步表明，XGBoost 模型比其他两个替代模型更适合预测 LV 舒张动态和估计被动参数。需要进一步的研究来调查 XGBoost 如何用于在多物理和多尺度框架中模拟心脏泵功能。