INI Fickle Heart Workshop 2024

The conference:

An unprecedented amount of patient data is now available from the clinic and the internet of things. Complex computer models have been developed that capture multiscale cardiac physiology in both animal experiments and in human patients. Machine learning methods for combining these models and data have now enabled the development of cardiac digital twins that can track patients through time, and forecast their disease progression and therapy outcomes.

Cardiac digital twins bring together physics and physiology-based modelling; uncertainty quantification tools for calibration and forecasting; methods for simulation acceleration; model analysis;  and data forecasts from AI/ML, statistics, and applied mathematics. This Newton Institute Fickle Heart Workshop will build on the 2019 program that focused on uncertainty quantification (UQ), to explore how UQ is being enabled by methodological advances in AI/ML, and how these techniques are being applied in digital twin applications.