Prediction of Left Ventricular Mechanics Using Machine Learning
Prediction of Left Ventricular Mechanics Using Machine Learning
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DOI：
10.3389/fphy.2019.00117
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发表时间：
2019-09-06
影响因子：
3.1
通讯作者：
Guccione, Julius M.
中科院分区：
文献类型：
--
作者：
Dabiri, Yaghoub;Van der Velden, Alex;Guccione, Julius M.
本文的目的是提供一个实时左心室（LV）力学模拟器，使用机器学习（ML）。对具有不同材料特性的LV进行有限元（FE）模拟以获得训练集。超弹性纤维增强材料模型被用来描述心肌的被动行为，在心肌梗死。在心脏收缩期间，由肌纤维收缩引起的心脏的主动行为被添加到被动组织。主动和被动属性支配LV本构方程。这些机械性能进行了改变，使用最佳拉丁超立方体实验设计，以获得不同的主动性能（体积和压力预测）和不同的被动性能（应力预测）的训练有限元模型。对于LV压力的预测，我们使用了极限梯度增强（XGboost）和Cubist，XGboost用于预测LV压力、容量以及LV应力。ML获得的LV压力和容积结果与FE计算结果相似。ML结果可以捕获LV压力的形状以及LV压力-容积环。Cubist的预测结果比XGBoost更平滑。平均绝对误差如下：XGBoost体积：1.734 +/- 0.584 ml，XGBoost压力：1.544 +/- 0.298 mmHg，Cubist体积：1.495 +/- 0.260 ml，Cubist压力：1.623 +/- 0.191 mmHg，肌纤维应力：0.334 +/- 0.228 kPa，交叉肌纤维应力：075 +/- 0.024 kPa，剪切应力：0.050 +/- 0.032 kPa。仿真结果表明，ML可以预测LV力学比有限元方法快得多。ML模型可以用作预测LV行为的工具。基于大量主题训练我们的ML模型可以提高其对真实的世界应用的可预测性。
The goal of this paper was to provide a real-time left ventricular (LV) mechanics simulator using machine learning (ML). Finite element (FE) simulations were conducted for the LV with different material properties to obtain a training set. A hyperelastic fiber-reinforced material model was used to describe the passive behavior of the myocardium during diastole. The active behavior of the heart resulting from myofiber contractions was added to the passive tissue during systole. The active and passive properties govern the LV constitutive equation. These mechanical properties were altered using optimal Latin hypercube design of experiments to obtain training FE models with varied active properties (volume and pressure predictions) and varied passive properties (stress predictions). For prediction of LV pressures, we used eXtreme Gradient Boosting (XGboost) and Cubist, and XGBoost was used for predictions of LV pressures, volumes as well as LV stresses. The LV pressure and volume results obtained from ML were similar to FE computations. The ML results could capture the shape of LV pressure as well as LV pressure-volume loops. The results predicted by Cubist were smoother than those from XGBoost. The mean absolute errors were as follows: XGBoost volume: 1.734 +/- 0.584 ml, XGBoost pressure: 1.544 +/- 0.298 mmHg, Cubist volume: 1.495 +/- 0.260 ml, Cubist pressure: 1.623 +/- 0.191 mmHg, myofiber stress: 0.334 +/- 0.228 kPa, cross myofiber stress: 0.075 +/- 0.024 kPa, and shear stress: 0.050 +/- 0.032 kPa. The simulation results show ML can predict LV mechanics much faster than the FE method. The ML model can be used as a tool to predict LV behavior. Training of our ML model based on a large group of subjects can improve its predictability for real world applications.