Predicting Occlusion Myocardial Infarctions in the Emergency Department Using Artificial Intelligence
Our AI model identified OMI in ED patients with chest pain with an AUC of 95%, doubling sensitivity compared with the STEMI criteria at the same specificity. Using the model could reduce time to intervention, as only about 1 in 20 OMI cases currently receive timely angiography.
Authors
- Anders Björkelund (ORCID: https://orcid.org/0000-0001-7426-914X)
- Jonas Björk (ORCID: https://orcid.org/0000-0003-1883-2000)
- Ulf Ekelund (ORCID: https://orcid.org/0000-0002-0951-1582)
- Arash Mokhtari (ORCID: https://orcid.org/0000-0002-6808-3442)
- Axel Nyström (ORCID: https://orcid.org/0000-0001-6657-5681)
- Jakob Lundager Forberg (ORCID: https://orcid.org/0000-0002-8048-0352)
- Henrik Wagner
- Mattias Ohlsson
Institutions
- Lund University (SE)
- Swedish Institute for Health Economics (SE)
- Helsingborgs lasarett (SE)
- Skåne University Hospital (SE)
- Halmstad University (SE)
Publication Details
- Journal
- Journal of the American College of Emergency Physicians Open
- Published
- 2026-01-09
- DOI
- https://doi.org/10.1016/j.acepjo.2025.100299
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00