Natalia A. Trayanova

This paper introduces a framework for probabilistic cardiac digital twins that enables uncertainty quantification through joint probability density learning and conditional regression.

This study combines cardiac magnetic resonance and patient-specific computational fluid dynamics to improve stroke risk assessment by showing that normalizing hemodynamic features with stroke volume, rather than ejection fraction, better differentiates stroke from control patients.