An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

• Published in: The Lancet (British edition) Vol. 394; no. 10201; pp. 861 - 867
• Main Authors: Attia, Zachi I, Noseworthy, Peter A, Lopez-Jimenez, Francisco, Asirvatham, Samuel J, Deshmukh, Abhishek J, Gersh, Bernard J, Carter, Rickey E, Yao, Xiaoxi, Rabinstein, Alejandro A, Erickson, Brad J, Kapa, Suraj, Friedman, Paul A
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07.09.2019; Elsevier Limited
• Subjects: Accuracy; Adult; Aged; Algorithms; Anticoagulants; Artificial intelligence; Artificial neural networks; Atrial Fibrillation - diagnosis; Atrial Flutter - diagnosis; Cardiac arrhythmia; Case-Control Studies; Congestive heart failure; Datasets; EKG; Electrocardiography; Electrocardiography - methods; Female; Fibrillation; Flutter; Heart; Humans; Hypotheses; Identification methods; Laboratories; Learning algorithms; Machine learning; Male; Middle Aged; Mortality; Neural networks; Neural Networks, Computer; Population; Predictive Value of Tests; Retrospective Studies; Rhythm; ROC Curve; Sensitivity; Sinuses; Stroke; Supine position; Training

Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying p...