Biventricular modelling for investigating ventricular arrhythmias in silico

Abstract Background Ventricular arrhythmias have a significant incidence rate worldwide and may potentially lead to sudden cardiac death in severe cases. Despite therapeutic advancements, the intricate mechanisms dictating these arrhythmias remain elusive. Biventricular modelling and deep learning provide a way to investigate the complex electrophysiological substrates underlying cardiac arrhythmogenesis. Purpose This study aims to develop and validate a patient-specific biventricular computational modelling pipeline for simulating ventricular arrhythmias in silico. We provide a comprehensive platform for understanding the initiation, propagation, and treatment response of various ventricular arrhythmias. Methods To facilitate large-scale computational experiments and personalised model predictions within clinical timeframes, it is imperative to establish a rapid and reproducible modelling framework. First, we constructed biventricular volumetric models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) data from the Cardiac Atlas Project (2018 Atria Segmentation Challenge), and then we calculate biventricular coordinates to facilitate data registration between measures, and fibre and fibrosis inclusion. We also apply the pipeline to cardiac magnetic resonance (CMR) data and electrocardiogram recordings from UK Biobank. Results We have successfully developed a pipeline for constructing biventricular volumetric models and efficiently processed biventricular coordinates (Figure). By systematically exploring interactions between ventricular regions, the model may identify critical factors contributing to arrhythmia susceptibility and guide personalised therapeutic strategies. Furthermore, the model may provide insights into the efficacy of antiarrhythmic interventions, ablation techniques, and device therapies, facilitating the development of precision medicine approaches. Conclusions We demonstrate a pipeline for constructing patient-specific biventricular computational models at scale from imaging data and apply this to LGE-MRI data and UK Biobank CMR data. These biventricular models enable investigation of the complex mechanisms of ventricular arrhythmias, providing novel insights for clinical decision-making and treatment strategies.