  Cardiac digital twins are computational tools capturing key functional and
anatomical characteristics of patient hearts for investigating disease
phenotypes and predicting responses to therapy. When paired with large-scale
computational resources and large clinical datasets, digital twin technology
can enable virtual clinical trials on virtual cohorts to fast-track therapy
development. Here, we present an automated pipeline for personalising
ventricular anatomy and electrophysiological function based on routinely
acquired cardiac magnetic resonance (CMR) imaging data and the standard 12-lead
electrocardiogram (ECG). Using CMR-based anatomical models, a sequential
Monte-Carlo approximate Bayesian computational inference method is extended to
infer electrical activation and repolarisation characteristics from the ECG.
Fast simulations are conducted with a reaction-Eikonal model, including the
Purkinje network and biophysically-detailed subcellular ionic current dynamics
for repolarisation. For each patient, parameter uncertainty is represented by
inferring a population of ventricular models rather than a single one, which
means that parameter uncertainty can be propagated to therapy evaluation.
Furthermore, we have developed techniques for translating from reaction-Eikonal
to monodomain simulations, which allows more realistic simulations of cardiac
electrophysiology. The pipeline is demonstrated in a healthy female subject,
where our inferred reaction-Eikonal models reproduced the patient's ECG with a
Pearson's correlation coefficient of 0.93, and the translated monodomain
simulations have a correlation coefficient of 0.89. We then apply the effect of
Dofetilide to the monodomain population of models for this subject and show
dose-dependent QT and T-peak to T-end prolongations that are in keeping with
large population drug response data.
