Alberto Zingaro

The paper uses AI-enhanced cardiac digital twins to assess drug proarrhythmic risk by propagating experimental uncertainty and simulating extreme exposures, finding sex-specific arrhythmogenic thresholds for loperamide.

This paper presents a multiscale atrial digital twin framework that integrates electrophysiology, mechanics, and circulation to simulate normal and pathological atrial function, showing how electrical changes affect mechanical and hemodynamic outcomes.

This paper develops sex-specific cardiac emulators to predict drug-induced QT interval prolongation in real-time, achieving high accuracy and drastically reducing computational time for drug safety assessments.

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.