A machine learning approach using Gaussian process emulation accurately predicted left ventricular ejection fraction (R2 = 0.92) in virtual models of healthy and aortic-banded hypertensive rats.
A novel machine learning framework using Gaussian process emulation enables accurate prediction of left ventricular function and parameter inference in complex biophysical cardiac models.
Effect estimate: R2 0.92
Cardiac contraction is the result of integrated cellular, tissue and organ function. Biophysical in silico cardiac models offer a systematic approach for studying these multi-scale interactions. The computational cost of such models is high, due to their multi-parametric and nonlinear nature. This has so far made it difficult to perform model fitting and prevented global sensitivity analysis (GSA) studies. We propose a machine learning approach based on Gaussian process emulation of model simulations using probabilistic surrogate models, which enables model parameter inference via a Bayesian history matching (HM) technique and GSA on whole-organ mechanics. This framework is applied to model healthy and aortic-banded hypertensive rats, a commonly used animal model of heart failure disease. The obtained probabilistic surrogate models accurately predicted the left ventricular pump function ( R 2 = 0.92 for ejection fraction). The HM technique allowed us to fit both the control and diseased virtual bi-ventricular rat heart models to magnetic resonance imaging and literature data, with model outputs from the constrained parameter space falling within 2 SD of the respective experimental values. The GSA identified Troponin C and cross-bridge kinetics as key parameters in determining both systolic and diastolic ventricular function. This article is part of the theme issue ‘Uncertainty quantification in cardiac and cardiovascular modelling and simulation’.
Longobardi et al. (Sun,) conducted a other in Heart failure. Gaussian process emulation was evaluated on Left ventricular pump function (ejection fraction) (R2 0.92). A machine learning approach using Gaussian process emulation accurately predicted left ventricular ejection fraction (R2 = 0.92) in virtual models of healthy and aortic-banded hypertensive rats.