Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models

International audiencePersonalised computational models of theheart are of increasing interest for clinical applica-tions due to their discriminative and predictive abili-ties. However, the simulation of a single heartbeat witha 3D cardiac electromechanical model can be long andcomputationally expensive, which makes some practicalapplications, such as the estimation of model parame-ters from clinical data (the personalisation), very slow.Here we introduce an original multidelity approachbetween a 3D cardiac model and a simplied "0D" ver-sion of this model, which enables to get reliable (andextremely fast) approximations of the global behaviorof the 3D model using 0D simulations. We then usethis multidelity approximation to speed-up an ecientparameter estimation algorithm, leading to a fast andcomputationally ecient personalisation method of the3D model. In particular, we show results on a cohort of121 dierent heart geometries and measurements. Fi-nally, an exploitable code of the 0D model with scriptsto perform parameter estimation will be released to thecommunity