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Predicting Occlusion Myocardial Infarctions in the Emergency Department Using Artificial Intelligence.

Axel Nyström1,2, Anders Björkelund2, Henrik Wagner3

  • 1Department of Laboratory Medicine, Lund University, Lund, Sweden.

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|January 22, 2026
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) model effectively predicts acute coronary occlusion myocardial infarction (OMI) in emergency department patients with chest pain. This AI tool demonstrates improved sensitivity over STEMI criteria, potentially reducing intervention times for OMI cases.

Keywords:
ECGartificial intelligenceocclusion myocardial infarction

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Area of Science:

  • Cardiology
  • Artificial Intelligence in Medicine
  • Emergency Medicine

Background:

  • Acute coronary occlusion myocardial infarction (OMI) is a critical condition requiring rapid diagnosis and intervention.
  • Current diagnostic criteria, such as ST-elevation myocardial infarction (STEMI), may miss a significant proportion of OMI cases.
  • Timely intervention for OMI is often delayed, contributing to adverse patient outcomes.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for predicting OMI in emergency department (ED) patients presenting with chest pain.
  • To utilize readily available early ED assessment data, including electrocardiogram (ECG), medical history, and initial lab values.
  • To compare the AI model's performance against established STEMI criteria.

Main Methods:

  • A deep-learning AI model was developed using data from 24,511 adult ED patients with chest pain across 5 Swedish hospitals.
  • OMI cases were identified through register data and health record review; STEMI cases bypassing the ED were excluded.
  • The AI model was trained on ECG data, optionally combined with other early ED information, and internally validated.

Main Results:

  • The AI model achieved a high area under the receiver operating characteristic (AUC) of 95.3% for OMI prediction.
  • At 97.4% specificity, the AI model demonstrated a sensitivity of 62%, significantly higher than the 27% sensitivity of STEMI criteria.
  • Only 5.4% of identified OMI cases received timely angiography, highlighting a critical gap in current care pathways.

Conclusions:

  • The developed AI model shows excellent performance in identifying OMI in ED chest pain patients, outperforming STEMI criteria in sensitivity.
  • Implementation of this AI model could expedite diagnosis and reduce delays in initiating interventions for OMI.
  • The findings suggest a potential to improve patient outcomes by addressing the current delays in OMI treatment.