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Artificial Intelligence and Atrial Fibrillation
  • Ojasav Sehrawat,
  • Anthony Kashou,
  • Peter Noseworthy
Ojasav Sehrawat
Mayo Clinic Department of Cardiovascular Medicine

Corresponding Author:sehrawat.ojasav@mayo.edu

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Anthony Kashou
Mayo Clinic Department of Cardiovascular Medicine
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Peter Noseworthy
Mayo Clinic Department of Cardiovascular Medicine
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In the context of atrial fibrillation (AF), traditional clinical practices have thus far fallen short in several domains such as identifying patients at risk of incident AF or patients with concomitant undetected paroxysmal AF. Novel approaches leveraging artificial intelligence have the potential to provide new tools to deal with some of these old problems. In this review we focus on the roles of artificial intelligence-enabled ECG pertaining to AF, potential roles of deep learning (DL) models in the context of current knowledge gaps, as well as limitations of these models. One key area where DL models can translate to better patient outcomes is through automated ECG interpretation. Further, we overview some of the challenges facing AF screening and the harms and benefits of screening. In this context, a unique model was developed to detect underlying hidden AF from sinus rhythm and is discussed in detail with its potential uses. Knowledge gaps also remain regarding the best ways to monitor patients with ESUS and who would benefit most from oral anticoagulation. The AI-enabled AF model is one potential way to tackle this complex problem as it could be used to identify a subset of high-risk ESUS patients likely to benefit from empirical oral anticoagulation. Role of DL models assessing AF burden from long duration ECG data is also discussed as a way of guiding management. There is a trend towards the use of consumer-grade wristbands and watches to detect AF from photoplethysmography data. However, ECG currently remains the gold standard to detect arrythmias including AF. Lastly, role of adequate external validation of the models and clinical trials to study true performance is discussed.
29 Dec 2021Submission Checks Completed
29 Dec 2021Assigned to Editor
29 Dec 2021Reviewer(s) Assigned
17 Jan 2022Review(s) Completed, Editorial Evaluation Pending
17 Jan 2022Editorial Decision: Revise Minor
03 Feb 20221st Revision Received
07 Feb 2022Submission Checks Completed
07 Feb 2022Assigned to Editor
07 Feb 2022Reviewer(s) Assigned
01 Mar 2022Review(s) Completed, Editorial Evaluation Pending
01 Mar 2022Editorial Decision: Accept
15 Mar 2022Published in Journal of Cardiovascular Electrophysiology. 10.1111/jce.15440