Work

Automated interpretations of single-lead electrocardiograms predict incident atrial fibrillation: The VITAL-AF trial

Year: 2024

Type: article

Source: Heart Rhythm

DOI: 10.1016/j.hrthm.2024.04.085

Authors: Daniel C. Pipilas, Shaan Khurshid, Mostafa A. Al-Alusi, Steven J. Atlas, Jeffrey M. Ashburner, Leila H. Borowsky, David D. McManus, Daniel E. Singer, Steven A. Lubitz, Yuchiao Chang, Patrick T. Ellinor

Institutions: Harvard University, Massachusetts General Hospital, University of Massachusetts Chan Medical School

Language: English

Topic: Atrial Fibrillation Management and Outcomes

Subfield: Cardiology and Cardiovascular Medicine

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: American Heart Association, European Commission, National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: G102488861, G1445764998, Precision Medicine in Ischemic Stroke and Atrial Fibrillation (PreMISe-AF), G3553811666, Genomics and Epigenetics of Atrial Fibrillation, G3897532131, Identifying primary care patients at increased risk of atrial fibrillation for screening interventions, Machine Learning Artificial Intelligence Early Detection Stroke Atrial Fibrillation, Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation, G8767518440, Deep Learning to Identify Digital Biomarkers of Cardiovascular Disease, Electrocardiogram-based deep learning and decision analysis to improve atrial fibrillation risk estimation, IDENTIFICATION OF COMMON GENETIC VARIANTS FOR ATRIAL FIBRILLATION AND PR INTERVAL, Using Electrocardiogram Genetics to Inform Arrhythmia Risk, Integrated, Multidisciplinary Training in Cardiovascular Research, Genomics of Cardiac Arrhythmias

OpenAlex ID: W4396217603

FWCI: 0.834

Cites: 21

Cited by: 3

Related to: 10