An AI-ECG algorithm for atrial fibrillation risk: steps towards clinical implementation

Affiliations

¹ Department of Epidemiology, and Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, 3015 CN, Netherlands. Electronic address: f.j.wolters@erasmusmc.nl.

Comment

Frank J Wolters. Lancet. 2020.

. 2020 Jul 25;396(10246):235-236.

doi: 10.1016/S0140-6736(20)31062-X.

¹ Department of Epidemiology, and Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, 3015 CN, Netherlands. Electronic address: f.j.wolters@erasmusmc.nl.

No abstract available

Comment in

An AI-ECG algorithm for atrial fibrillation risk: steps towards clinical implementation - Authors' reply.
Noseworthy PA, Attia ZI, Carter RE, Yao X, Friedman PA. Noseworthy PA, et al. Lancet. 2020 Jul 25;396(10246):236-237. doi: 10.1016/S0140-6736(20)31064-3. Lancet. 2020. PMID: 32711795 No abstract available.

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