Yaariv Khaykin: AI ECG Analysis Uncovers Hidden AF Associated With Future Stroke Risk
Yaariv Khaykin, Associate Professor at Temerty Faculty of Medicine, University of Toronto, President and Physician Leader of Pace Cardiology, shared a post on LinkedIn about a recent article by Arjun K Butani et al, published in medRxiv, adding:
“AI-detected asymptomatic atrial fibrillation carries meaningful stroke and cardiovascular risk, according to a UK Biobank analysis.
Researchers used machine learning algorithms to identify AF in ECG recordings from individuals without prior AF diagnosis, then tracked incident ischemic stroke and major adverse cardiovascular events over follow-up.
Key finding: Asymptomatic AF detected by AI was associated with increased risk of both outcomes, raising clinical questions about screening strategies and management thresholds in asymptomatic populations.
This adds nuance to the growing body of literature on subclinical AF detection.
While the study doesn’t prescribe clinical action for every AI-detected case, it quantifies the risk signal—useful context as we integrate AI ECG interpretation into practice with devices like Myant Skiin and others, and as we consider which patients warrant closer monitoring or intervention.”
Title: AI-Detected Asymptomatic Atrial Fibrillation and Risk of Incident Ischemic Stroke and Cardiovascular Events: A UK Biobank Study
Authors: Arjun K Butani, Zareen Farukhi, David Brüggemann, Pamela Rist, Felix C Tanner, Olga V Demler
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