Patient-Specific and Large-Scale Computational Modelling: Insights for Cardiac Digital Twins

Computational cardiac modelling and cardiac digital twins involve the creation of physiologically accurate computational models of the heart using patient- and population-specific clinical data from baseline, imaging, and diagnostic testing. By facilitating detailed investigations into complex and theoretical cardiovascular scenarios, these in silico cardiac models can provide granular insights that are difficult or high-risk to obtain using traditional clinical techniques, thereby enhancing understanding of cardiac function and the information available for clinical decision-making. We use ARCHER2 and the cardiac arrhythmia research package (CARP) to model atrial and ventricular electrophysiology, as well as aortic blood flow, to investigate underlying mechanisms and treatment of cardiac arrhythmias such as atrial fibrillation and ventricular tachycardia, and aortic stenosis. Using both large-scale population-based studies and patient-specific investigations of individual patients, we simulate different treatment strategies in silico, with the aim of developing novel treatment approaches. Further integration with techniques in machine learning allows predictions to be made on realistic timescales for clinical applications.