Using artificial intelligence-enabled electrocardiogram to predict cardiac resynchronization therapy outcomes of left bundle branch area pacing.

Journal: Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology

PMID: 38227817

Abstract

No abstract available for this article.

Authors

Jingjing Chen

Department of Cardiovascular Medicine, Affiliated Hospital of Guizhou Medical University, Guiyang, China.
Fatima M Ezzeddine

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Xiaoke Liu

Department of Cardiovascular Medicine, Mayo Clinic, LaCrosse, WI, USA.
Freddy Del-Carpio Munoz

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Paul Friedman

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Yong-Mei Cha

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.

Keywords

Artificial Intelligence Bundle of His Cardiac Pacing, Artificial Cardiac Resynchronization Therapy Electrocardiography Heart Conduction System Heart Failure Humans Treatment Outcome Ventricular Function, Left

External Resources

View on PubMed Access via DOI PubMed (38227817)

Using artificial intelligence-enabled electrocardiogram to predict cardiac resynchronization therapy outcomes of left bundle branch area pacing.

Abstract

Authors

Keywords

External Resources

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