CJEM Visual Abstract – Machine learning for the diagnosis of acute coronary syndrome using a 12-lead ECG: a systematic review

In Infographics by Samuel WilsonLeave a Comment

For the October 2023 issue of CJEM, we collaborated with their team to present “Machine learning for the diagnosis of acute coronary syndrome using a 12-lead ECG: a systematic review”​1​​ in a visually simplified format.

Many of us see the interpretation that is included on the top or side of each ECG, with common practice being to approach these diagnoses with caution. We often rely on our clinicians interpretation of the 12-lead, instead. This raises a few good questions: how good are we as EM clinicians, and how good is machine learning technology becoming?

This study, by Zworth et al, reviewed the evidence available on the diagnostic accuracy of machine for the diagnosis of acute coronary syndrome using a 12-lead ECG. Authors also looked at how machine learning algorithms compares to an EM clinician, with a deep dive into the quality of evidence available on the topic. For a summary of what the authors found, a .pdf version of a visual abstract on the topic can be found below:

CJEM visual abstract
  1. 1.
    Zworth M, Kareemi H, Boroumand S, Sikora L, Stiell I, Yadav K. Machine learning for the diagnosis of acute coronary syndrome using a 12-lead ECG: a systematic review. Can J Emerg Med. Published online September 4, 2023:818-827. doi:10.1007/s43678-023-00572-5

Samuel Wilson

Sam is a third-year Emergency Medicine FRCPC resident at The Ottawa Hospital. He is the CanadiEM/CJEM Infographic editor, interested in PoCUS, trauma, knowledge dissemination, and all things chess.