Original Research ARTICLE
Prediction of Left Ventricular Mechanics using Machine Learning
- 1California Medical Innovations Institute, United States
- 2University of California, San Francisco, United States
- 3Dassault Systemes (United States), United States
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: 1.083 ml, XGBoost pressure: 1.668 mmHg, Cubist volume: 0.153 ml, Cubist pressure: 0.48 mmHg, myofiber stress: 0.334 kPa, cross myofiber stress: 0.075 kPa and shear stress: 0.050 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.
Keywords: machine learning, Left ventricle, Finite element method, XGBoost, Cubist
Received: 12 May 2019;
Accepted: 07 Aug 2019.
Copyright: © 2019 Dabiri, Velden, Sack, Choy, Kassab and Guccione. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Julius Guccione, University of California, San Francisco, San Francisco, 94143, California, United States, Julius.Guccione@ucsf.edu