基于人工智能的冠状动脉 CT 血管造影术与定量冠状动脉血管造影术的冠状动脉狭窄量化对比。

IF 3.8 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiology. Cardiothoracic imaging Pub Date : 2023-12-01 DOI:10.1148/ryct.230124
James Dundas, Jonathon A Leipsic, Stephanie Sellers, Philipp Blanke, Patricia Miranda, Nicholas Ng, Sarah Mullen, David Meier, Mariama Akodad, Janarthanan Sathananthan, Carlos Collet, Bernard de Bruyne, Olivier Muller, Georgios Tzimas
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Quantitative analysis of coronary stenosis severity at CCTA was performed using an AI-based coronary stenosis quantification (AI-CSQ) software service. Blinded comparison between QCA and AI-CSQ was measured on a per-vessel and per-patient basis. Results The per-vessel AI-CSQ diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 80%, 88%, 86%, 65%, and 94%, respectively, for diameter stenosis (DS) 50% or greater; and 78%, 92%, 91%, 47%, and 98%, respectively, for DS 70% or greater. The areas under the receiver operating characteristic curve (AUCs) to predict DS of 50% or greater and 70% or greater on a per-vessel basis were 0.92 (95% CI: 0.88, 0.95; <i>P</i> < .001) and 0.93 (95% CI: 0.89, 0.97; <i>P</i> < .001), respectively. The AUCs to predict DS of 50% or greater and 70% or greater on a per-patient basis were 0.93 (95% CI: 0.88, 0.97; <i>P</i> < .001) and 0.88 (95% CI: 0.81, 0.94; <i>P</i> < .001), respectively. 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引用次数: 0

摘要

目的 通过比较冠状动脉 CT 血管造影 (CCTA) 的量化狭窄严重程度与有创定量冠状动脉血管造影 (QCA) 的参考标准,评估基于人工智能 (AI) 的新工具的性能。材料与方法 该二次事后分析包括来自三项大型临床试验(AFFECTS、P3、REFINE)的 120 名参与者(平均年龄为 59.7 岁 ± 10.8 [SD];73 [60.8%] 名男性,47 [39.2%] 名女性),他们都接受了 CCTA 和带有 QCA 的有创冠状动脉造影检查。使用基于人工智能的冠状动脉狭窄量化(AI-CSQ)软件服务对 CCTA 的冠状动脉狭窄严重程度进行定量分析。QCA和AI-CSQ之间的盲比测量以每个血管和每个患者为基础。结果 每血管 AI-CSQ 诊断灵敏度、特异性、准确性、阳性预测值和阴性预测值分别为:直径狭窄 (DS) 50% 或以上时,分别为 80%、88%、86%、65% 和 94%;直径狭窄 (DS) 70% 或以上时,分别为 78%、92%、91%、47% 和 98%。预测每个血管的 DS 为 50%或以上和 70% 或以上的接收者操作特征曲线下面积(AUC)分别为 0.92 (95% CI: 0.88, 0.95; P < .001) 和 0.93 (95% CI: 0.89, 0.97; P < .001)。预测每名患者 DS 为 50%或以上和 70% 或以上的 AUC 分别为 0.93 (95% CI: 0.88, 0.97; P < .001) 和 0.88 (95% CI: 0.81, 0.94; P < .001)。结论 与 QCA 相比,CCTA 的 AI-CSQ 对每个患者和每个血管都有很高的诊断性能,对狭窄的检测具有很高的灵敏度。关键词CT血管造影 心脏 冠状动脉 本文有补充材料。以 CC BY 4.0 许可发布。
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Artificial Intelligence-based Coronary Stenosis Quantification at Coronary CT Angiography versus Quantitative Coronary Angiography.

Purpose To evaluate the performance of a new artificial intelligence (AI)-based tool by comparing the quantified stenosis severity at coronary CT angiography (CCTA) with a reference standard derived from invasive quantitative coronary angiography (QCA). Materials and Methods This secondary, post hoc analysis included 120 participants (mean age, 59.7 years ± 10.8 [SD]; 73 [60.8%] men, 47 [39.2%] women) from three large clinical trials (AFFECTS, P3, REFINE) who underwent CCTA and invasive coronary angiography with QCA. Quantitative analysis of coronary stenosis severity at CCTA was performed using an AI-based coronary stenosis quantification (AI-CSQ) software service. Blinded comparison between QCA and AI-CSQ was measured on a per-vessel and per-patient basis. Results The per-vessel AI-CSQ diagnostic sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 80%, 88%, 86%, 65%, and 94%, respectively, for diameter stenosis (DS) 50% or greater; and 78%, 92%, 91%, 47%, and 98%, respectively, for DS 70% or greater. The areas under the receiver operating characteristic curve (AUCs) to predict DS of 50% or greater and 70% or greater on a per-vessel basis were 0.92 (95% CI: 0.88, 0.95; P < .001) and 0.93 (95% CI: 0.89, 0.97; P < .001), respectively. The AUCs to predict DS of 50% or greater and 70% or greater on a per-patient basis were 0.93 (95% CI: 0.88, 0.97; P < .001) and 0.88 (95% CI: 0.81, 0.94; P < .001), respectively. Conclusion AI-CSQ at CCTA demonstrated a high diagnostic performance compared with QCA both on a per-patient and per-vessel basis, with high sensitivity for stenosis detection. Keywords: CT Angiography, Cardiac, Coronary Arteries Supplemental material is available for this article. Published under a CC BY 4.0 license.

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