AI-Assisted Interpretation of Cardiac CT in the Emergency Department

Rövid összefoglaló (regiszter, angolul)

This prospective, pragmatic, randomized controlled trial is designed to evaluate the impact of an artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) interpretation tool (Angiomics) on emergency physicians' diagnostic performance and clinical decision-making in patients presenting with acute chest pain. CCTA is a critical diagnostic modality for suspected acute coronary syndrome (ACS) in the emergency department (ED). Accurate interpretation often requires experienced radiologists, who may not always be available, particularly during off-hours. The introduction of AI-based interpretation tools into clinical workflow has the potential to enhance diagnostic accuracy, increase physician confidence, reduce delays in decision-making, and improve efficiency of resource utilization. However, evidence regarding the real-world effectiveness of such AI tools in the ED setting remains limited. Eligible participants will include adults aged 18 years or older presenting to the ED with chest pain and classified as intermediate risk (HEART score 4-6). Participants will be randomized into two groups: (1) AI-assisted CCTA interpretation, in which emergency physicians interpret scans with access to AI results; and (2) standard interpretation, in which emergency physicians interpret CCTA without AI support. In both groups, physicians will document the presence of stenosis in the four major coronary arteries (LM, LAD, LCX, RCA). After informed consent, participants are randomised in a 1:1 ratio before CCTA acquisition to AI-assisted or standard CCTA interpretation. Diagnostic confidence is recorded using four categories: very high, high, moderate and low. The enrolment target is 620 participants, with 310 participants per group. This is a single-centre trial conducted at Severance Hospital, Seoul, Republic of Korea. The primary outcome is the negative predictive value (NPV) of CCTA interpretation at the patient level, comparing AI-assisted versus standard interpretations against the reference standard of blinded consensus readings by board-certified radiologists. Secondary outcomes include per-patient diagnostic performance (sensitivity, specificity, PPV and AUROC), per-vessel diagnostic performance, emergency physician diagnostic confidence and protocol deviation rate. Other outcomes include cardiology consultation rate and time to disposition decision. Enrollment and follow-up will be conducted at Severance Hospital over a 24-month period following IRB approval. The results are expected to provide evidence for the clinical utility and effectiveness of AI-based CCTA interpretation in the ED and to guide integration of AI into emergency care in order to optimize patient outcomes and healthcare efficiency.

Mit vállalsz

• Vizsgálat típusa: Beavatkozással járó
• Tervezett résztvevők: 620
• 2025. dec. 29. és az elsődleges befejezés (2027. jún.) között
• 1 helyszín, 1 országban
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