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Cardiac in silico clinical trials can virtually assess the safety andefficacy of therapies using human-based modelling and simulation. Thesetechnologies can provide mechanistic explanations for clinically observedpathological behaviour. Designing virtual cohorts for in silico trials requiresexploiting clinical data to capture the physiological variability in the humanpopulation. The clinical characterisation of ventricular activation and thePurkinje network is challenging, especially non-invasively. Our study aims topresent a novel digital twinning pipeline that can efficiently generate andintegrate Purkinje networks into human multiscale biventricular models based onsubject-specific clinical 12-lead electrocardiogram and magnetic resonancerecordings. Essential novel features of the pipeline are the human-basedPurkinje network generation method, personalisation considering ECG R waveprogression as well as QRS morphology, and translation from reduced-orderEikonal models to equivalent biophysically-detailed monodomain ones. Wedemonstrate ECG simulations in line with clinical data with clinicalimage-based multiscale models with Purkinje in four control subjects and twohypertrophic cardiomyopathy patients (simulated and clinical QRS complexes withPearson's correlation coefficients > 0.7). Our methods also considered possibledifferences in the density of Purkinje myocardial junctions in theEikonal-based inference as regional conduction velocities. These differencestranslated into regional coupling effects between Purkinje and myocardialmodels in the monodomain formulation. In summary, we demonstrate a digital twinpipeline enabling simulations yielding clinically-consistent ECGs with clinicalCMR image-based biventricular multiscale models, including personalisedPurkinje in healthy and cardiac disease conditions.

Digital Twinning of the Human Ventricular Activation Sequence to  Clinical 12-lead ECGs and Magnetic Resonance Imaging Using Realistic Purkinje  Networks for in Silico Clinical Trials