CARDIOPHYDAT - CARDIOvascular PHYsics and DATa integration for digital twins

The research project CARDIOPHYDAT (CARDIOvascular PHYsics and DATa integration for digital twins) focuses on developing innovative core methodologies for the accurate subject-specific calibration of cardiac electromechanical models from in vivo clinical measurements. 

In particular, efficient and robust personalised cardiac digital twins based on physics-informed machine learning methodologies will be generated to estimate the local distribution of patient-specific biophysical and mechanical properties and infer clinical biomarkers. In addition, the proposed methodology will be employed for the assessment of fibrotic scars in cardiac tissue, which play a significant role in many heart diseases.  

This set of methods will significantly contribute to the development of a unique, personalised cardiovascular modelling approach that will leverage leading-edge mathematical methodologies and advance fundamental research in cardiovascular modeling. Furthermore, by integrating clinical data with computational models, the project has practical applications in clinical settings - e.g., enabling the estimation of crucial clinical biomarkers that cannot be directly measured using conventional methods - to support diagnosis and prediction of therapeutic outcomes within clinical time constraints.

 

Project funding

Funding EUR 333.579,-