Cardiac digital twins powered by machine learning revealed that conduction velocity is linked to adverse clinical outcomes, cardiac function, and lifestyle and mental health phenotypes.
Cardiac digital twins generated at scale from multimodal data can uncover underlying electrophysiological mechanisms, such as conduction velocity and repolarization conductance, that explain variations in ECG phenotypes and predict clinical outcomes.
were associated with cardiac function, lifestyle and mental health phenotypes, and CV was also linked with adverse clinical outcomes. Our study demonstrates how CDT development at scale reveals biological insights across populations.
Qian et al. (2025) studied this question. Cardiac digital twins powered by machine learning revealed that conduction velocity is linked to adverse clinical outcomes, cardiac function, and lifestyle and mental health phenotypes.