Why the study?
Identifying undiagnosed AF in ESUS patients is crucial, but cost and invasiveness often limit the use of guideline-recommended implantable loop recorders.
Does a 12-lead ECG-based artificial intelligence model accurately predict near-term atrial fibrillation in patients with embolic stroke of undetermined source?
Does a 12-lead ECG-based artificial intelligence model accurately predict near-term atrial fibrillation in patients with embolic stroke of undetermined source?
An AI model using sinus rhythm 12-lead ECGs may help predict near-term atrial fibrillation and guide implantable loop recorder decisions in patients with embolic stroke of undetermined source.
Embolic stroke of undetermined source (ESUS) composes about 17% of ischemic strokes, and identifying undiagnosed atrial fibrillation (AF) is crucial for determining treatment strategies.1 Although guidelines recommended implantable loop recorders (ILR),2 cost and invasiveness often limit their use. This study investigates the efficacy of an artificial intelligence (AI) model predicting AF from sinus rhythm (SR) electrocardiograms (ECG) in ESUS patients and its impact on ILR implantation decisions.
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Tomura et al. (2025) studied this question.
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