ArticleJACC. Advances2026
AI-Enhanced Electrocardiogram for Detection of Occlusive Myocardial Infarction in High-Risk Non-ST-Segment Elevation Acute Coronary Syndrome.
Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- AI-Enabled Electrocardiogram for Rule-Out of NSTEMI Across Clinically Challenging Presentations.JACC. Advances · 2026Article
- PMcardio-Assisted ECG Interpretation in Non-ST-Elevation Acute Coronary Syndrome: A Case Report.Clinical case reports · 2026Article
- Evidence for Electrocardiographic Patterns Identifying Acute Coronary Occlusion in Non-ST-Elevation Acute Coronary Syndromes: A Scoping Review.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2026Article
- Acute Coronary Occlusion in NSTEMI Patients: Prevalence, Clinical Characteristics and the Potential Role of Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2026Article
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Authors and funding
22 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhether an artificial intelligence-enhanced electrocardiogram (AI-ECG) improves detection of occlusive myocardial infarction (OMI) in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) compared with standard of care (SOC) remains uncertain.
objectivesThe objective of the study was to evaluate an AI-ECG model for detecting OMI among patients with suspected high-risk NSTE-ACS using invasive coronary angiography (ICA) as the reference.
methodsConsecutive adults undergoing emergent ICA under the institutional level 2 protocol for suspected high-risk NSTE-ACS (2022-2024) were included. Patients with ST-elevation myocardial infarction were excluded. Initial 12-lead ECGs were retrospectively analyzed using an investigational AI-ECG model (Queen of Hearts, Powerful Medical) validated against ICA findings. OMI was defined as an angiographic culprit lesion with TIMI flow grade 0 to 2 or TIMI flow grade 3 with marked troponin elevation or new regional wall motion abnormality.
resultsAmong 224 patients, OMI was present in 129 (58%). AI-ECG identified 75 (58%) of these as OMI and classified 96 patients (43%) as OMI overall. For rule in, specificity was 78% (95% CI: 68%-86%) and positive predictive value 78% (95% CI: 69%-86%). For rule out, sensitivity was 58% (95% CI: 49%-66%) and negative predictive value 57% (95% CI: 48%-66%). Serial ECGs reduced false negatives from 42% to 34%. Compared with the standard of care, an AI-augmented triage approach reduced false positives from 42% to 22% (P < 0.001).
conclusionsIn high-risk NSTE-ACS, AI-ECG improved rule in accuracy for OMI, potentially enhancing early identification of patients requiring urgent ICA. However, rule out performance from initial ECG alone was limited, supporting the need for serial ECGs and clinical judgment.
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