Natalia Trayanova explains how digital twins are being used to plan EP ablations

Digital twin technology could reshape electrophysiology (EP) by allowing clinicians to test and optimize ablation strategies on a virtual replica of an individual patient’s heart before treating them. The concept of creating digital twins of patients has evolved from a vague concept to something that is actually being used for clinical research. 

The results so far are very promising, explained Natalia Trayanova, PhD, professor of biomedical engineering and director of AI research in health and medicine at the Data Science and AI Institute at Johns Hopkins University. She discussed her team's research and data from other recent studies with Cardiovascular Business in a new video interview.

Digital twins are distinct from conventional artificial intelligence (AI), Trayanova emphasized. While machine-learning algorithms identify patterns across large populations, a digital twin represents the behavior of a specific patient’s organ and is intended to support personalized treatment.

Her team builds digital hearts from patient-specific data to evaluate arrhythmia risk and determine optimal ablation strategies. Applications have included persistent atrial fibrillation (AFib), particularly in patients with substantial substrate remodeling and fibrosis, as well as ventricular tachycardia (VT) in patients with ischemic cardiomyopathy and other forms of cardiac disease.