The Lead Episode 119: A Discussion of Near-Term Prediction of Sustained Ventricular Arrhythmias Applying Artificial Intelligence to Single-Lead Ambulatory Electrocardiogram LIVE at HRX
Description
Please join Digital Education Committee Vice-Chair, Tina Baykaner, MD, MPH, of Stanford University, as she is joined by Heart Rhythm Society President Mina K. Chung, MD, FHRS, of the Cleveland Clinic, and Konstantinos C. Siontis, MD, FHRS of the May Clinic. The three met up in Altanta at HRX 2025 for this stimulating coversation.

This study evaluated whether artificial intelligence applied to single-lead ambulatory ECGs could predict imminent sustained ventricular arrhythmias. Using deep learning models, the researchers demonstrated that AI could identify subtle ECG features preceding arrhythmic events, enabling accurate short-term risk prediction. The findings suggest a potential role for AI-enhanced ECG monitoring to improve early detection and prevention of life-threatening ventricular arrhythmias.
Learning Objectives
  • Predict lethal ventricular arrhythmias, leveraging AI to prevent SCD.
Article Authors and Podcast Contributors
Article Authors
Laurent Fiorina
Tanner Carbonati
Kumar Narayanan
Jia Li
Christine Henry
Jagmeet Singh
Eloi Marijon

Podcast Contributors

Tina Baykaner, MD, MPH | Stanford University
Mina K. Chung, MD, FHRS | Cleveland Clinic
Konstantinos C. Siontis, MD, FHRS | Mayo Clinic

Summary
Availability:
On-Demand
Expires on Sep 18, 2028
Cost:
FREE
 


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