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Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: Physicians vs. AI (Queen of Hearts by

Steven Shroyer1, Sumeru Mehta1, Nandish Thukral2

  • 1Department of Emergency Medicine, Greater San Antonio Emergency Physicians, Methodist Hospital System, San Antonio, TX, USA.

The American Journal of Emergency Medicine
|August 5, 2025
PubMed

Insights

Physicians struggle with interpreting complex ECGs for occlusive myocardial infarction (OMI), leading to potential misclassification. An AI algorithm, Queen of Hearts AI, showed significantly higher accuracy in identifying OMI requiring catheterization laboratory activation (CLA).

Area of Science:

  • Cardiology
  • Medical Diagnostics
  • Artificial Intelligence in Medicine

Background:

  • Accurate electrocardiogram (ECG) interpretation is vital for identifying occlusive myocardial infarction (OMI) and guiding immediate catheterization laboratory activation (CLA).
  • STEMI-equivalent and STEMI-mimic ECG patterns present diagnostic challenges, potentially leading to misclassification of OMI.
  • The diagnostic accuracy of physicians for these complex ECG presentations remains largely unknown.

Purpose of the Study:

  • To assess physician accuracy in interpreting STEMI-equivalent and STEMI-mimic ECGs for CLA decisions.
  • To compare physician performance against a machine learning-based artificial intelligence algorithm, Queen of Hearts AI (QoH AI).

Main Methods:

  • Fifty-three electrophysiologists and 42 cardiologists interpreted 18 ECGs (including STEMI-equivalents, STEMI-mimics, STEMI, and normal controls).
  • Interpretations aimed to identify OMI requiring immediate CLA.
  • ECGs were also analyzed by QoH AI, with interpretations compared to a reference standard (angiography, troponin, echocardiography, clinical follow-up).

Main Results:

  • Physician interpretation accuracy was similar between electrophysiologists (65.6%) and cardiologists (65.5%), but significantly lower than QoH AI (88.9%).
  • Physicians frequently misclassified ECGs such as de Winter, Transient STEMI, Hyperacute T-wave OMI, and bundle branch block.
  • QoH AI demonstrated higher accuracy, with fewer misclassifications, primarily in complex cases like left bundle branch block with OMI.

Conclusions:

  • Physicians exhibit frequent misinterpretations of STEMI-equivalent and STEMI-mimic ECGs, impacting critical CLA decisions.
  • QoH AI shows superior diagnostic accuracy, offering potential to reduce missed OMI cases and unnecessary CLA.
  • Further prospective studies are warranted to validate these findings in real-world clinical settings.
Abstract

Keywords:
Cath lab activationECG accuracyMyocardical infarctionPCISTEMISTEMI equivalentSTEMI mimic

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