Artificial intelligence-based opportunistic detection of coronary artery stenosis on aortic computed tomography angiography in emergency department patients with acute chest pain.

European heart journal open Pub Date : 2023-09-07 eCollection Date: 2023-09-01 DOI:10.1093/ehjopen/oead088
Carl G Glessgen, Marianthi Boulougouri, Jean-Paul Vallée, Stéphane Noble, Alexandra Platon, Pierre-Alexandre Poletti, Jean-François Paul, Jean-François Deux
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引用次数: 1

Abstract

Aims: To evaluate a deep-learning model (DLM) for detecting coronary stenoses in emergency room patients with acute chest pain (ACP) explored with electrocardiogram-gated aortic computed tomography angiography (CTA) to rule out aortic dissection.

Methods and results: This retrospective study included 217 emergency room patients (41% female, mean age 67.2 years) presenting with ACP and evaluated by aortic CTA at our institution. Computed tomography angiography was assessed by two readers, who rated the coronary arteries as 1 (no stenosis), 2 (<50% stenosis), or 3 (≥50% stenosis). Computed tomography angiography was categorized as high quality (HQ), if all three main coronary arteries were analysable and low quality (LQ) otherwise. Curvilinear coronary images were rated by a DLM using the same system. Per-patient and per-vessel analyses were conducted. One hundred and twenty-one patients had HQ and 96 LQ CTA. Sensitivity, specificity, positive predictive value, negative predictive value (NPV), and accuracy of the DLM in patients with high-quality image for detecting ≥50% stenoses were 100, 62, 59, 100, and 75% at the patient level and 98, 79, 57, 99, and 84% at the vessel level, respectively. Sensitivity was lower (79%) for detecting ≥50% stenoses at the vessel level in patients with low-quality image. Diagnostic accuracy was 84% in both groups. All 12 patients with acute coronary syndrome (ACS) and stenoses by invasive coronary angiography (ICA) were rated 3 by the DLM.

Conclusion: A DLM demonstrated high NPV for significant coronary artery stenosis in patients with ACP. All patients with ACS and stenoses by ICA were identified by the DLM.

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急诊科急性胸痛患者主动脉计算机断层扫描血管造影术中基于人工智能的冠状动脉狭窄机会性检测。
目的:评估一种用于检测急诊室急性胸痛(ACP)患者冠状动脉狭窄的深度学习模型(DLM),该模型通过心电图门控主动脉计算机断层摄影血管造影术(CTA)进行探索,以排除主动脉夹层。方法和结果:这项回顾性研究包括217名急诊室患者(41%为女性,平均年龄67.2岁),他们在我们机构接受了ACP检查并通过主动脉CTA进行了评估。两位读者对计算机断层扫描血管造影术进行了评估,他们对冠状动脉的评分分别为1(无狭窄)、2(结论:DLM对ACP患者的显著冠状动脉狭窄显示出高NPV。所有ACS患者和ICA狭窄患者都通过DLM进行了识别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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