Cardiac digital twins (DTs) are patient‑specific mathematical, statistical, and computational models of the heart, and are rapidly becoming a transformative tool in cardiovascular science and medicine. They are virtual replicas of an individual’s heart that continuously assimilate data into sophisticated models giving insights into the physiological state of a patient. They offer unprecedented opportunities for monitoring, diagnosis, therapy planning, device design, risk stratification, and predicting disease progression. However, despite major advances in cardiac imaging, electrophysiology, biomechanics, and simulation capability, the mathematical and statistical foundations required to build reliable, interpretable, and clinically actionable cardiac digital twins remain substantially underdeveloped.
This workshop will bring together practitioners (cardiac modellers and clinicians) with mathematicians, statisticians, and machine learning researchers with the aim of
- identifying the core mathematical, statistical, and computational challenges underlying cardiac digital twin development, including problems of model calibration (including sensitivity, identifiability, real-time calibration methods), uncertainty quantification (including uncertainty due to model discrepancy, transfer learning between patients), model development (the use of multi-fidelity ensembles), and decision support (making decision under uncertainty, in the presence of missing data, robustness etc).
- defining a research agenda for the mathematical and statistical development of cardiac digital twins methodology.
- initiating new interdisciplinary collaborations that lead to foundational research outputs, working groups, and grant proposals.
