Journal of cardiovascular computed tomography · 2026
Diagnostic performance of artificial intelligence-based coronary artery analysis in the emergency department: A multicenter study.
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Lee Ji Won, Han Kyunghwa, Kim Jin Young, Lee Kye Ho, Im Dong Jin, Kim Na Young, Choi Byoung Wook, Schöbinger Max, Pak Seongyong, Hur Jin
Аннотация
RATIONALE AND OBJECTIVES: On-premise artificial intelligence (AI) software may enable rapid interpretation of coronary CT angiography (CCTA) in the emergency department (ED), although its diagnostic performance relative to invasive coronary angiography (ICA) remains uncertain. We aimed to evaluate the diagnostic performance of an AI-based coronary artery analysis software for detecting obstructive coronary artery disease (CAD) (CAD-RADS ≥3) in ED patients with acute chest pain, using expert consensus and ICA as separate reference standards.
MATERIALS AND METHODS: In this retrospective multicenter study, 1193 patients with acute chest pain who underwent CCTA across four academic EDs (January 2019-August 2024) were analyzed using on-premise AI software. Diagnostic performance for detecting obstructive CAD was evaluated in two cohorts, using expert consensus (n = 885) and ICA (n = 308) as reference standards. Overall, 342 patients (28.7%) had obstructive CAD.
RESULTS: Using expert consensus, AI achieved a sensitivity of 79.5% and a negative predictive value (NPV) of 95.4%. With ICA as the reference, sensitivity and NPV were 94.6% and 89.2%, respectively. AI and expert radiologists showed comparable sensitivity (94.6% vs 95.2%; P = 0.56), NPV (89.2% vs 91.4%; P = 0.20), and area under the curve (0.87 vs 0.87; P = 0.86), although AI demonstrated lower specificity (68.0% vs 78.7%) and positive predictive value (PPV) (81.8% vs 87.2%) (both P < 0.001).
CONCLUSION: On-premise AI provides high sensitivity and NPV for excluding obstructive CAD, comparable to expert radiologists, although specificity and PPV were significantly lower. These findings support its role as a second-reader and clinical decision-support tool in emergency settings.
MATERIALS AND METHODS: In this retrospective multicenter study, 1193 patients with acute chest pain who underwent CCTA across four academic EDs (January 2019-August 2024) were analyzed using on-premise AI software. Diagnostic performance for detecting obstructive CAD was evaluated in two cohorts, using expert consensus (n = 885) and ICA (n = 308) as reference standards. Overall, 342 patients (28.7%) had obstructive CAD.
RESULTS: Using expert consensus, AI achieved a sensitivity of 79.5% and a negative predictive value (NPV) of 95.4%. With ICA as the reference, sensitivity and NPV were 94.6% and 89.2%, respectively. AI and expert radiologists showed comparable sensitivity (94.6% vs 95.2%; P = 0.56), NPV (89.2% vs 91.4%; P = 0.20), and area under the curve (0.87 vs 0.87; P = 0.86), although AI demonstrated lower specificity (68.0% vs 78.7%) and positive predictive value (PPV) (81.8% vs 87.2%) (both P < 0.001).
CONCLUSION: On-premise AI provides high sensitivity and NPV for excluding obstructive CAD, comparable to expert radiologists, although specificity and PPV were significantly lower. These findings support its role as a second-reader and clinical decision-support tool in emergency settings.