ArticleEuropean heart journal. Digital health2024
International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06570759 (Time for a DIagnostic Paradigm Shift From ST-elevation/Non-ST-elevation to OCClUsion/Non-occLusion Myocardial infarcTion), which is not on this map. Cited by 75 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Time for a DIagnostic Paradigm Shift From ST-elevation/Non-ST-elevation to OCClUsion/Non-occLusion Myocardial infarcTion
Who cites it
75 citing papers in PubMed.
- Human vs Artificial Intelligence in Detecting Coronary Occlusions in ST-Elevation Myocardial Infarction: Retrospective Study.JMIR cardio · 2026Article
- Three doors, three locks: a domain-specific framework for the residual door-to-balloon gap in STEMI care : Matters Arising, commentary on Krishnan et al., Int J Emerg Med 2026;19:113.International journal of emergency medicine · 2026Article
- Occlusion myocardial infarction in type I NSTEMI: prevalence, characteristics, management, and long-term outcome.Clinical research in cardiology : official journal of the German Cardiac Society · 2026Article
- Improved Detection of Acute Coronary Occlusion Myocardial Infarction by an Artificial Intelligence Electrocardiogram Model in Swedish Emergency Departments.Journal of the American College of Emergency Physicians open · 2026Article
- PMcardio-Assisted ECG Interpretation in Non-ST-Elevation Acute Coronary Syndrome: A Case Report.Clinical case reports · 2026Article
- Physicians' perspectives on artificial intelligence in electrocardiography in clinical practice: a qualitative study.BMC medical ethics · 2026Article
- Contemporary Management of Acute Coronary Syndromes in the Cardiac Intensive Care Unit: From Rapid Diagnosis to Early Secondary Prevention.Journal of cardiovascular development and disease · 2026Review
- ECG-based detection of occlusion myocardial infarction: a dedicated deep neural network versus multimodal large language models and physicians - a retrospective diagnostic accuracy study.BMC emergency medicine · 2026Article
- Article
- Specificity of the Occlusion Myocardial Infarction-Trained PMcardio "Queen of Hearts" AI-ECG in Inflammatory Myopericardial Syndromes.JACC. Advances · 2026Article
- Review
- Review
- From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Review
- Sex-Consistent Performance of an AI-Enabled ECG for Acute Myocardial Infarction: The ROMIAE Study.JACC. Advances · 2026Article
- [Artificial intelligence in internal medicine exemplified by cardiology].Innere Medizin (Heidelberg, Germany) · 2026Review
- A deep learning ECG model for identification and localization of occlusion myocardial infarction.Nature communications · 2026Article
- Acute Coronary Occlusion in NSTEMI Patients: Prevalence, Clinical Characteristics and the Potential Role of Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2026Article
- A large-scale 12-lead electrocardiogram dataset for acute coronary syndrome prediction containing 19,955 ECGs.Scientific data · 2026Article
- Artificial intelligence-enhanced electrocardiography for acute myocardial infarction detection: a systematic review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
15 more citing papers are in PubMed but not listed here.
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Authors and funding
24 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: A majority of acute coronary syndromes (ACS) present without typical ST elevation. One-third of non-ST-elevation myocardial infarction (NSTEMI) patients have an acutely occluded culprit coronary artery [occlusion myocardial infarction (OMI)], leading to poor outcomes due to delayed identification and invasive management. In this study, we sought to develop a versatile artificial intelligence (AI) model detecting acute OMI on single-standard 12-lead electrocardiograms (ECGs) and compare its performance with existing state-of-the-art diagnostic criteria. Methods and results: An AI model was developed using 18 616 ECGs from 10 543 patients with suspected ACS from an international database with clinically validated outcomes. The model was evaluated in an international cohort and compared with STEMI criteria and ECG experts in detecting OMI. The primary outcome of OMI was an acutely occluded or flow-limiting culprit artery requiring emergent revascularization. In the overall test set of 3254 ECGs from 2222 patients (age 62 ± 14 years, 67% males, 21.6% OMI), the AI model achieved an area under the curve of 0.938 [95% confidence interval (CI): 0.924-0.951] in identifying the primary OMI outcome, with superior performance [accuracy 90.9% (95% CI: 89.7-92.0), sensitivity 80.6% (95% CI: 76.8-84.0), and specificity 93.7 (95% CI: 92.6-94.8)] compared with STEMI criteria [accuracy 83.6% (95% CI: 82.1-85.1), sensitivity 32.5% (95% CI: 28.4-36.6), and specificity 97.7% (95% CI: 97.0-98.3)] and with similar performance compared with ECG experts [accuracy 90.8% (95% CI: 89.5-91.9), sensitivity 73.0% (95% CI: 68.7-77.0), and specificity 95.7% (95% CI: 94.7-96.6)]. Conclusion: The present novel ECG AI model demonstrates superior accuracy to detect acute OMI when compared with STEMI criteria. This suggests its potential to improve ACS triage, ensuring appropriate and timely referral for immediate revascularization.
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