AI can now identify atrial fibrillation through sinus rhythm

Affiliations

¹ Centre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, SA 5000, Australia. Electronic address: jeroen.hendriks@adelaide.edu.au.
² Institute of Cardiovascular Science, University of Birmingham, Birmingham, UK; Department of Cardiology, University Hospital Birmingham, Birmingham, UK.

Comment

Jeroen M L Hendriks et al. Lancet. 2019.

. 2019 Sep 7;394(10201):812-813.

doi: 10.1016/S0140-6736(19)31719-2. Epub 2019 Aug 1.

¹ Centre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, SA 5000, Australia. Electronic address: jeroen.hendriks@adelaide.edu.au.
² Institute of Cardiovascular Science, University of Birmingham, Birmingham, UK; Department of Cardiology, University Hospital Birmingham, Birmingham, UK.

No abstract available

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An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.
Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. Attia ZI, et al. Lancet. 2019 Sep 7;394(10201):861-867. doi: 10.1016/S0140-6736(19)31721-0. Epub 2019 Aug 1. Lancet. 2019. PMID: 31378392