Personalization of a Hemodynamic Cardiac Digital Twin: An Echocardiogram based Approach

Cardiac computational models that replicate cardiac functionalities like a ‘digital twin’, can play important roles in serving clinical and research requirements, ranging from presurgical planning to predictive analysis. Personalization of such digital twins are non-trivial and computationally exhaustive due to the uncertainties of fitting mechanistic models to clinical measurements for individual patients. In this paper, we propose a method to personalize the hemodynamics functionality of a cardiac digital twin using a particle swarm optimization (PSO) framework that tunes the cardiac chamber properties based on subject-specific echocardiogram (Echo) and electrocardiogram (ECG) data. Parameters derived from ECG like information related to time instances of pumping action in cardiac chambers and Echo parameters like left ventricle end systolic and diastolic diameters and volumes are used to personalize the cardiac chamber parameters of an existing lumped cardiac hemodynamics model. Using this strategy, personalized hemodynamics parameters are generated for a healthy and two diseased subjects, suffering from cardiac Amyloidosis and Grade I Diastolic dysfunction. The proposed method of non-invasive modality-based (Echo and ECG) personalizing cardiac functionality can play a critical role in facilitating the circulatory hemodynamics model for clinical settings and aid in enabling precision medicine applications.