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-A Discussion of Near-Term Prediction of Sustained Ventricular Arrythmias Applying Artificial Intelligence to Single-Lead Ambulatory Electrocardiogram LIVE at HRX (Speaker Information)

A Discussion of Near-Term Prediction of Sustained Ventricular Arrythmias Applying Artificial Intelligence to Single-Lead Ambulatory Electrocardiogram LIVE at HRX (Speaker Information)

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Pdf Summary

This discussion centers on the article "Near-Term Prediction of Sustained Ventricular Arrhythmias Applying Artificial Intelligence to Single-Lead Ambulatory Electrocardiogram," published in Heart Rhythm Journal by Laurent Fiorina et al. The session, hosted by Dr. Tina Baykaner (Stanford University), along with contributors Dr. Konstantinos C. Siontis (Mayo Clinic) and Dr. Mina K. Chung (Cleveland Clinic), explores the application of AI technology to predict imminent sustained ventricular arrhythmias using data from single-lead ambulatory ECG recordings.<br /><br />The focus is on leveraging artificial intelligence to analyze continuous ECG signals for early detection and near-term prediction of life-threatening ventricular arrhythmias, which are crucial for timely intervention and improving patient outcomes. The discussion highlights how single-lead ambulatory ECGs, being less invasive and more accessible, combined with AI algorithms, could transform arrhythmia monitoring outside hospital settings.<br /><br />Contributors bring expertise in electrophysiology and clinical cardiology, adding clinical context to the AI model's potential utility. Disclosures note the speakers’ relationships with medical device companies, research funding from NIH and the American Heart Association, and intellectual property interests related to relevant technologies.<br /><br />Overall, the session emphasizes the promise of integrating AI with ambulatory ECG data to enhance predictive capabilities for ventricular arrhythmias in outpatient populations, potentially facilitating earlier interventions and reducing sudden cardiac death risk. The collaboration of academic and clinical experts ensures a comprehensive evaluation of the AI approach’s clinical implications, as well as considerations for future research and real-world application.

Keywords

Artificial Intelligence

Ventricular Arrhythmias

Near-Term Prediction

Single-Lead Ambulatory ECG

Electrocardiogram Analysis

Heart Rhythm Journal

Sustained Ventricular Arrhythmias

Electrophysiology

Cardiology

Sudden Cardiac Death Prevention