Editorial: Data assimilation in cardiovascular medicine: Merging experimental measurements with physics-based computational models

Physics-based computational models, constructed based on sound theoretical principles, can give meaningful insight into the complex interrelationship among different parts of the cardiovascular system, such as the heart, vessels and valves. To date, diverse types of physicsbased cardiovascular models (Shi et al., 2018; Leong et al., 2019a; Niederer et al., 2019; Ong et al., 2020; Geddes et al., 2022), spanning the disciplines of electrophysiology, electromechanics, solid mechanics, fluid dynamics and cardiovascular reflex, have greatly enhanced our understanding of cardiovascular diseases. However, most of these models used population-based parameters, and thus had significant model uncertainties due to huge intraand interpatient variability. Rapid advancement of imaging and hemodynamic monitoring technologies has made available useful patient-specific information for personalization of cardiovascular models. Integrating imaging and hemodynamic measurements with physics-based computational models not only enables more accurate prediction of physiological or pathological status for individual patients, but also allows for computation of hemodynamic variables that are challenging to measure experimentally. In addition, patient-specific simulations can provide important insights into the mechanism underlying the disease progression. This Research Topic focuses on using imaging and hemodynamic measurements for geometry reconstruction, parameterization, or validation of cardiovascular models, with the aim to generate personalized models that could reproduce clinical observations. There is a total of four published articles relevant to basic and clinical studies, covering: 1) deep learning-based framework for feature identification and segmentation; 2) effect of patientOPEN ACCESS