Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs

• Published in: Circulation. Arrhythmia and electrophysiology Vol. 12; no. 9; p. e007284
• Main Authors: Attia, Zachi I., Friedman, Paul A., Noseworthy, Peter A., Lopez-Jimenez, Francisco, Ladewig, Dorothy J., Satam, Gaurav, Pellikka, Patricia A., Munger, Thomas M., Asirvatham, Samuel J., Scott, Christopher G., Carter, Rickey E., Kapa, Suraj
• Format: Journal Article
• Language: English
• Published: United States American Heart Association, Inc 01.09.2019; Lippincott Williams & Wilkins
• Subjects: Adolescent; Adult; Aged; Algorithms; Artificial Intelligence; Deep Learning; Electrocardiography - methods; Female; Humans; Male; Middle Aged; Neural Networks, Computer; Original; Retrospective Studies; Young Adult; coronary disease; hypertension; neural network; artificial intelligence; electrocardiography

BACKGROUND:Sex and age have long been known to affect the ECG. Several biologic variables and anatomic factors may contribute to sex and age-related differences on the ECG. We hypothesized that a convolutional neural network (CNN) could be trained through a process called deep learning to predict a...