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EHRA 24: AI-Enabled Single-Lead ECG Can Unmask Conduction Tissue Disease

Published: 19 Apr 2024

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EHRA 24 - In this short interview, we are joined by Dr Laurent Fiorina (Cardiovascular Institute Paris-Sud (ICPS), FR) joins us to discuss the findings from a clinical investigation into diagnosing conduction tissue disease using an AI-enabled single-lead coronary ECG device. 

This observational, prospective clinical trial aims to investigate the performance of an artificial intelligence algorithm interpreting The aim of this study is to evaluate the ability of an artificial intelligence (AI)-enabled single-lead ambulatory electrocardiogram (ECG) to identify patients who  experienced asystole due to sinus pause or complete heart block in the past 2 weeks.

The study included 319,396 unselected 14-day ambulatory ECG recordings (mean age 60.5 ± 17.8; 60% female) collected from five Independent Diagnostic Testing Facilities. Researchers developed a deep learning-based model using the last 24 hours of each recording to identify patients with daytime sinus pause of ≥3 seconds, prolonged sinus pause at any time of ≥6 seconds, or complete heart block documented during the previous 13 days of monitoring.

Interview Questions:

  1. What is the importance of this study?
  2. What was the study design and patient population?
  3. What are the key findings?
  4. What are some of the key challenges and opportunities in AI AF detection in 2024?
  5. What are your take-home messages?

Recorded on-site at EHRA in Berlin, 2024. 

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