Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke

Circ Arrhythm Electrophysiol. 2024 Oct;17(10):e012959. doi: 10.1161/CIRCEP.124.012959. Epub 2024 Aug 28.

Shaan Khurshid^{1

2

3}, Samuel F Friedman⁴, Shinwan Kany⁵, Rahul Mahajan^{3

6}, Ashby C Turner^{7

3}, Steven A Lubitz^{1

2

3}, Mahnaz Maddah⁴, Patrick T Ellinor^{1

2

3}, Christopher D Anderson^{3

6}

¹ Demoulas Center for Cardiac Arrhythmias (S. Khurshid, S.A.L., P.T.E.), Massachusetts General Hospital, Boston, MA.
² Cardiovascular Research Center (S. Khurshid, S.A.L., P.T.E.), Massachusetts General Hospital, Boston, MA.
³ Cardiovascular Disease Initiative (S. Khurshid, R.M., A.C.T., S.A.L., P.T.E., C.D.A.), Broad Institute of MIT and Harvard, Cambridge, MA.
⁴ Data Sciences Platform (S.F.F., M.M.), Broad Institute of MIT and Harvard, Cambridge, MA.
⁵ Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Germany (S. Kany).
⁶ Department of Neurology, Brigham and Women's Hospital, Boston, MA (R.M., C.D.A.).
⁷ Department of Neurology (A.C.T.), Massachusetts General Hospital, Boston, MA.

No abstract available

Keywords: anticoagulants; atrial fibrillation; deep learning; risk factors; stroke.

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