Personalised computer models of cardiac function, referred to as cardiac digital twins, are envisioned to play an important role in clinical precision therapies of cardiovascular diseases. A major obstacle hampering clinical translation involves the significant computational costs involved in the personalisation of biophysically detailed mechanistic models that require the identification of high-dimensional parameter vectors. An important aspect to identify in electromechanics (EM) models are active mechanics parameters that govern cardiac contraction and relaxation. In this study, we present a novel, fully automated, and efficient approach for personalising biophysically detailed active mechanics models using a two-step multi-fidelity solution. In the first step, active mechanical behaviour in a given 3D EM model is represented by a purely phenomenological, low-fidelity model, which is personalised at the organ scale by calibration to clinical cavity pressure data. Then, in the second step, median traces of nodal cellular active stress, intracellular calcium concentration, and fibre stretch are generated and utilised to personalise the desired high-fidelity model at the cellular scale using a 0D model of cardiac EM. Our novel approach was tested on a cohort of seven human left ventricular (LV) EM models, created from patients treated for aortic coarctation (CoA). Goodness of fit, computational cost, and robustness of the algorithm against uncertainty in the clinical data and variations of initial guesses were evaluated. We demonstrate that our multi-fidelity approach facilitates the personalisation of a biophysically detailed active stress model within only a few (2 to 4) expensive 3D organ-scale simulations—a computational effort compatible with clinical model applications.

Physiological processes are regulated by nonlinear dynamical systems. Various nonlinear measures have frequently been used for characterizing the complexity of fractal time signals to detect system features that cannot be derived from linear analyses. We analysed human balance dynamics ranging from simple standing to balancing on one foot with closed eyes to study the inherent methodological problems when applying fractal dimension analysis toreal-worldsignals. Higuchi dimension was used as an example. Choice of measurement and analysis parameters has a distinct influence on the computed dimension. Noise increases the fractional dimension which may be misinterpreted as a higher complexity of the signal. Publications without specifying the parameter setting, or without analysing the noise-sensitivity are not comparable to findings of others and therefore of limited scientific value.

Wind influences the jump length in ski jumping, which raises questions about the fairness. To counteract the wind problem, the International Ski Federation has introduced a wind compensation system in 2009: time-averaged wind velocity components tangential to the landing slope are obtained from several sites along the landing slope, and these data are used in a linear statistical model for estimating the jump length effect of wind. This is considered in the total score of the ski jump. However, it has been shown that the jump length effect estimates can be inaccurate and misleading. The present article introduces an alternative mathematical wind compensation approach that is based on an accurate mechanistic model of the flight phase. This estimates the jump length effect as difference between the jump length of the real ski jump at the given wind condition and the computed jump length of the simulated ski jump at calm wind. Inputs for the computer simulation are the initial flight velocity and aerodynamic coefficients of the real ski jump that can be obtained from kinematic and wind velocity data collected during the flight. The initial flight velocity is readily available from the kinematic data and inverse dynamics can be used to compute the aerodynamic coefficients. The accuracy of the estimated jump length effect of the mechanistic model-based approach depends only on the measurement errors in the kinematic and wind velocity data, but not on inaccuracies of an approach that is based on a linear statistical model.