Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study.

Journal: European heart journal
Published Date:

Abstract

BACKGROUND AND AIMS: Emerging evidence supports artificial intelligence-enhanced electrocardiogram (AI-ECG) for detecting acute myocardial infarction (AMI), but real-world validation is needed. The aim of this study was to evaluate the performance of AI-ECG in detecting AMI in the emergency department (ED).

Authors

  • Min Sung Lee
    Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Korea (Drs Jung, Ms Kang, Drs Son and H Lee, Ms Han, Ms Yoo, Drs Kwon, M Lee, and S Lee). Electronic address: lylm85@gmail.com.
  • Tae Gun Shin
    Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea.
  • Youngjoo Lee
    School of Mechanical Engineering, Hanyang University, Seoul, Korea.
  • Dong Hoon Kim
    Department of Emergency Medicine, Gyeongsang National University College of Medicine, Jinju, Republic of Korea.
  • Sung Hyuk Choi
    Department of Emergency Medicine, Korea University Guro Hospital, Seoul, Republic of Korea.
  • Hanjin Cho
    Department of Emergency Medicine, Korea University Ansan Hospital, 15355 Ansan-si, Republic of Korea.
  • Mi Jin Lee
    Department of Emergency Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.
  • Ki Young Jeong
    Department of Emergency Medicine, Kyung Hee University College of Medicine, Kyung Hee University Hospital, Seoul, Republic of Korea.
  • Won Young Kim
    Department of Emergency Medicine, Asan Medical Center, Seoul, Republic of Korea.
  • Young Gi Min
    Department of Emergency Medicine, Ajou University School of Medicine, Suwon, Republic of Korea.
  • Chul Han
    Department of Emergency Medicine, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
  • Jae Chol Yoon
    Department of Emergency Medicine, Research Institute of Clinical Medicine, Jeonbuk National University, Biomedical Research Institute, Jeonbuk National University Hospital, Jeonju, Republic of Korea.
  • Eujene Jung
    Department of Emergency Medicine, Chonnam National University Medical School, Gwangju, Republic of Korea.
  • Woo Jeong Kim
    College of Medicine, Jeju National University, Jeju, Republic of Korea.
  • Chiwon Ahn
    Department of Emergency Medicine, College of Medicine, Chung-Ang University, Seoul, Republic of Korea.
  • Jeong Yeol Seo
    Department of Emergency Medicine, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.
  • Tae Ho Lim
    Department of Emergency Medicine, College of Medicine, Hanyang University, 222 Wangsimni-ro, Seongdong-gu, Seoul, 04763, Republic of Korea. erthim@gmail.com.
  • Jae Seong Kim
    Department of Emergency Medicine, Incheon Sejong Hospital, Incheon, Republic of Korea.
  • Jeff Choi
    From the Division of General Surgery (J.C., K.M., D.I.H., J.D.F.), Department of Surgery, Department of Biomedical Data Science (J.C.), Stanford University; Program in Epithelial Biology (N.Y.L.), Stanford University School of Medicine; and Department of Computer Science (A.P., K.C.), Stanford University, Stanford, California.
  • Joon-Myoung Kwon
    Department of Emergency Medicine, Mediplex Sejong Hospital, Incheon, Korea.
  • Kyuseok Kim
    College of Medicine, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, Republic of Korea. Electronic address: dreinstein70@gmail.com.