Comparative Evaluation of Different Emulators for Cardiac Mechanics

This paper outlines a comparison of different emulation based approaches to the task of parameter inference in a biomechanical model of the left ventricle of the heart, where the emulation models can account for variations in left ventricle geometry. Models considered include Gaussian processes, neural networks and random forests. We are able to achieve accurate parameter estimation for two of the model parameters, while the extension of statistical emulation to the multi geometry case allows us to observe identifiability issues in some of the model parameters. This was not observed in our previous single geometry emulation studies. Overall, this study shows the ability to generalize the single geometry emulation strategy to multiple geometries, pushing us closer towards in clinic decision support systems.