Artificial neural networks for ECG interpretation in acute coronary syndrome: A scoping review.

Journal: The American journal of emergency medicine
PMID: 38936320

Abstract

INTRODUCTION: The electrocardiogram (ECG) is a crucial diagnostic tool in the Emergency Department (ED) for assessing patients with Acute Coronary Syndrome (ACS). Despite its widespread use, the ECG has limitations, including low sensitivity of the STEMI criteria to detect Acute Coronary Occlusion (ACO) and poor inter-rater reliability. Emerging ECG features beyond the traditional STEMI criteria show promise in improving early ACO diagnosis, but complexity hinders widespread adoption. The potential integration of Artificial Neural Networks (ANN) holds promise for enhancing diagnostic accuracy and addressing reliability issues in ECG interpretation for ACO symptoms.

Authors

  • Andrew J Bishop
    Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia. Electronic address: andrew.bishop@ambulance.vic.gov.au.
  • Ziad Nehme
    Ambulance Victoria, Melbourne, Victoria, Australia; Department of Paramedicine, Monash University, Melbourne, Victoria, Australia.
  • Shane Nanayakkara
    Department of Cardiology, Alfred Hospital, Melbourne, Victoria, Australia.
  • David Anderson
    Autonomous Systems and Connectivity, University of Glasgow, Glasgow, United Kingdom.
  • Dion Stub
    Department of Cardiology, Alfred Hospital, Melbourne, Victoria, Australia.
  • Benjamin N Meadley
    Ambulance Victoria, Doncaster, Victoria, Australia; Department of Paramedicine, Monash University, Frankston, Victoria, Australia.

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