Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches.

Journal: The Canadian journal of cardiology
PMID: 32220387

Abstract

BACKGROUND: Machine learning (ML) encompasses a wide variety of methods by which artificial intelligence learns to perform tasks when exposed to data. Although detection of myocardial infarction has been facilitated with introduction of troponins, the diagnosis of acute coronary syndromes (ACS) without myocardial damage (without elevation of serum troponin) remains subjective, and its accuracy remains highly dependent on clinical skills of the health care professionals. Application of a ML algorithm may expedite management of ACS for either early discharge or early initiation of ACS management. We aim to summarize the published studies of ML for diagnosis of ACS.

Authors

  • Patrick A Iannattone
    Division of Internal Medicine, McGill University Health Center, Montréal, Québec, Canada.
  • Xun Zhao
    Transplant AI Initiative, Ajmera Transplant Program, University Health Network, Toronto, ON, Canada.
  • Jacob VanHouten
    Departments of Internal Medicine and Preventive Medicine, Griffin Hospital, Derby, Connecticut, USA.
  • Akhil Garg
    Faculty of Medicine, McGill University, Montréal, Québec, Canada.
  • Thao Huynh
    Division of Cardiology, Department of Medicine, McGill University Health Center, Montréal, Québec, Canada. Electronic address: thao.huynhthanh@mail.mcgill.ca.

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