An automated computational pipeline for generating large-scale cohorts of patient-specific ventricular models in electromechanical in silico trials

BACKGROUND AND OBJECTIVE: In recent years, human in silico trials have gained significant traction as a powerful approach to evaluate the effects of drugs, clinical interventions, and medical devices. In silico trials not only minimise patient risks but also reduce reliance on animal testing. However, implementing in silico trials presents several time-consuming challenges. It requires the creation of large cohorts of virtual patients. Each virtual patient is described by their anatomy with a volumetric mesh and electrophysiological and mechanical dynamics through mathematical equations and parameters. Furthermore, simulated conditions need definition including stimulation protocols and therapy evaluation. For large virtual cohorts, this requires automatic and efficient pipelines for the generation of corresponding files. In this work, we present a computational pipeline to automatically create large virtual patient cohort files to conduct large-scale in silico trials through cardiac electromechanical simulations. METHODS: The pipeline automatically generates anatomical labels, volumetric meshes suited for electromechanical simulations, and all necessary fields and files for the simulations, including stimulation information, from unprocessed surface meshes and input parameters, without requiring training data. It also handles patient heterogeneity and supports integration with algorithms for Purkinje network generation and electrocardiogram personalisation. RESULTS: The pipeline was applied across several datasets to generate over 100 virtual patients. Simulations were performed to demonstrate its capacity to conduct in silico trials for virtual patients using verified and validated electrophysiology and electromechanics models for the context of use. The proposed pipeline demonstrated its adaptability to accommodate different types of ventricular geometries and mesh processing tools, ensuring its versatility in handling diverse clinical datasets. CONCLUSIONS: By establishing an automated framework for large scale simulation studies as required for in silico trials and providing open-source code, our work aims to support scalable, personalised cardiac simulations in research and clinical applications.