Artificial Intelligence-Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis.

Journal: Mayo Clinic proceedings

PMID: 34218880

OBJECTIVE: To develop an artificial intelligence (AI)-based tool to detect cardiac amyloidosis (CA) from a standard 12-lead electrocardiogram (ECG).

Martha Grogan

Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Francisco Lopez-Jimenez

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Michal Cohen-Shelly

Department of Cardiovascular Medicine Mayo Clinic Rochester MN.
Angela Dispenzieri

Division of Hematology, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Zachi I Attia

Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Omar F Abou Ezzedine

Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
Grace Lin

Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota.
Suraj Kapa

Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
Daniel D Borgeson

Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN.
Paul A Friedman

Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, Minnesota, USA.
Dennis H Murphree

Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA.

Amyloid Neuropathies, Familial Area Under Curve Artificial Intelligence Cardiomyopathies Early Diagnosis Electrocardiography Female Humans Male Middle Aged Neural Networks, Computer Predictive Value of Tests Retrospective Studies Time-to-Treatment United States

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