J Korean Med Sci.  2025 Mar;40(12):e105. 10.3346/jkms.2025.40.e105.

AI-ECG Supported Decision-Making for Coronary Angiography in Acute Chest Pain: The QCG-AID Study

• Park J
• Kim J
• Ahn S
• Cho Y
• Yoon YE

• KMID: 2566252
• DOI: http://doi.org/10.3346/jkms.2025.40.e105

Abstract

This pilot study evaluates an artificial intelligence (AI)-assisted electrocardiography (ECG) analysis system, QCG, to enhance urgent coronary angiography (CAG) decision-making for acute chest pain in the emergency department (ED). We retrospectively analyzed 300 ED cases, categorized as non-coronary chest pain (Group 1), acute coronary syndrome (ACS) without occlusive coronary artery disease (CAD) (Group 2), and ACS with occlusive CAD (Group 3). Six clinicians made urgent CAG decision using a conventional approach (clinical data and ECG) and a QCG-assisted approach (including QCG scores). The QCG-assisted approach improved correct CAG decisions in Group 2 (36.0% vs. 45.3%, P = 0.003) and Group 3 (85.3% vs. 90.0%, P = 0.017), with minimal impact in Group 1 (92.7% vs. 95.0%, P = 0.125). Diagnostic accuracy for ACS improved from 77% to 81% with QCG assistance and reached 82% with QCG alone, supporting AI's potential to enhance urgent CAG decisionmaking for ED chest pain cases.

Keyword

Artificial Intelligence; Electrocardiography; Chest Pain; Coronary Angiography; Acute Coronary Syndrome

Figure

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  Fig. 1 Subgroup analysis of clinical decision-making for urgent coronary angiography. Subgroup analyses by specialty: (A) Cardiology and (B) EM, revealed a consistent increase in appropriate decision-making with the QCG-assisted approach across all groups.EM = Emergency Medicine, QCG = artificial intelligence-based quantitative electrocardiography analysis, CAG = coronary angiography, ACS = acute coronary syndrome.

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