Peer-Reviewed Publication
JACC Adv2026;5(4):102671.April 1, 2026Journal Article
Performance of Artificial Intelligence-Powered ECG Analysis in Suspected ST-Segment Elevation Myocardial Infarction.
Scott W Sharkey1, Robert Herman2, Dawn R Witt3, Frank Aguirre4, Mehmet Yildiz5, David M Larson3, Avinash Murthy4, Heather S Rohm5, Stephen W Smith6, Will Belzer3, Jenny Chambers4, Ellen Cravero3, Seth Bergstedt3, Greg Kerola3, David Farmer4, Andrew Willett3, H Pendell Meyers7, Julia Harris3, Christopher VanHove3, Timothy D Henry5
1Minneapolis Heart Institute Foundation, Minneapolis, Minnesota, USA. Electronic address: scott.sharkey@allina.com.
2Cardiovascular Center Aalst, AZORG, Aalst, Belgium; Powerful Medical, Bratislava, Slovakia.
3Minneapolis Heart Institute Foundation, Minneapolis, Minnesota, USA.
4Prairie Heart Institute at St. John's Hospital, Springfield, Illinois, USA.
5Carl and Edyth Lindner Center for Research and Education at The Christ Hospital, Cincinnati, Ohio, USA.
6Department of Emergency Medicine, Hennepin County Medical Center and University of Minnesota, Minneapolis, Minnesota, USA.
7Department of Emergency Medicine, Carolinas Medical Center and Wake Forest University School of Medicine, Charlotte, North Carolina, USA.
Abstract
BACKGROUND: Artificial intelligence (AI)-based electrocardiogram (ECG) analysis has emerged as a promising adjunct to human ECG interpretation in suspected ST-segment elevation myocardial infarction (STEMI).
OBJECTIVES: To expand knowledge in this evolving field, the authors retrospectively analyzed the performance of a novel AI-ECG model in patients with cardiac catheterization laboratory activa…
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