Precision Cardiology based on Digital Twins

Cardiovascular diseases are often treated with implantable medical devices such as pacemakers or defibrillators. For optimal therapeutic success with these devices, it`s essential to customize their implantation and settings to provide the best treatment outcome for a specific patient. A promising approach to achieve these personalized precision therapies is through the use of virtual heart technologies (VHTs). These are computer models that can simulate the anatomy and electrical behavior of the heart in great detail. When VHTs are personalized and constructed using data from an individual, referred to as a "digital twin," they can accurately replicate the electrical events in that patient`s heart. However, to effectively utilize VHTs for therapy optimization in cardiovascular diseases, we must address existing calibration issues with patient data and ensure the reliability of predictions. Our research goal is to establish a workflow to automatically create mechanistic digital twins using non-invasive clinical image and measurement data. These twins would accurately represent the electrical behavior of the ventricles, the main chambers of the heart. The digital twins will be equipped with a personalized model of the ventricular conduction system, allowing them to replicate a normal heartbeat and therefore be specifically helpful for pacemaker therapies. For this purpose, we aim to develop automated workflows for VHTs in the form of digital twins from clinical data. The workflows include a real-time capable biophysically detailed ECG model, methods to quantify modeling uncertainties, and techniques to identify patient-specific model parameters. Resultant VHTs will be used to create and calibrate digital twins representing three different patient groups: healthy subjects, patients suffering from infarct-related ventricular tachycardias who are treated with ablation, and patients with conduction disorders treated with resynchronization therapy. We will validate these virtual cohorts and evaluate their ability to optimize therapeutic outcomes. Our quantification methods will estimate the probability with which treatment predictions from the VHTs can be trusted. The main innovation of this project is the novel workflow for generating personalized VHTs from clinical data. We aim to demonstrate the effectiveness of the digital twins in personalizing the electrical function of the heart and predicting therapeutic effects. We also intend to prove the credibility of these VHTs, laying the groundwork for simulation-based digital twins as crucial tools in precision cardiology. These technologies are expected to play a significant role in the future development of medical devices.