人工智能引导的冠状动脉计算机断层扫描血管造影与单光子发射计算机断层扫描检测临床实践中缺血情况的首次比较。

First comparison between artificial intelligence-guided coronary computed tomography angiography versus single-photon emission computed tomography testing for ischemia in clinical practice.

作者信息

Cho Geoffrey W, Sayed Sammy, D'Costa Zoee, Karlsberg Daniel W, Karlsberg Ronald P

机构信息

Department of Cardiology, David Geffen School of Medicine, University of California, Los Angeles.

Cardiovascular Research Foundation of Southern California.

出版信息

Coron Artery Dis. 2025 Aug 1;36(5):390-395. doi: 10.1097/MCA.0000000000001485. Epub 2024 Dec 20.

Abstract

BACKGROUND

Noninvasive cardiac testing with coronary computed tomography angiography (CCTA) and single-photon emission computed tomography (SPECT) are becoming alternatives to invasive angiography for the evaluation of obstructive coronary artery disease. We aimed to evaluate whether a novel artificial intelligence (AI)-assisted CCTA program is comparable to SPECT imaging for ischemic testing.

METHODS

CCTA images were analyzed using an artificial intelligence convolutional neural network machine-learning-based model, atherosclerosis imaging-quantitative computed tomography (AI-QCT) ISCHEMIA . A total of 183 patients (75 females and 108 males, with an average age of 60.8 years ± 12.3 years) were selected. All patients underwent AI-QCT ISCHEMIA -augmented CCTA, with 60 undergoing concurrent SPECT and 16 having invasive coronary angiograms. Eight studies were excluded from analysis due to incomplete data or coronary anomalies.

RESULTS

A total of 175 patients (95%) had CCTA performed, deemed acceptable for AI-QCT ISCHEMIA interpretation. Compared to invasive angiography, AI-QCT ISCHEMIA -driven CCTA showed a sensitivity of 75% and specificity of 70% for predicting coronary ischemia, versus 70% and 53%, respectively for SPECT. The negative predictive value was high for female patients when using AI-QCT ISCHEMIA compared to SPECT (91% vs. 68%, P  = 0.042). Area under the receiver operating characteristic curves were similar between both modalities (0.81 for AI-CCTA, 0.75 for SPECT, P  = 0.526). When comparing both modalities, the correlation coefficient was r  = 0.71 ( P  < 0.04).

CONCLUSION

AI-powered CCTA is a viable alternative to SPECT for detecting myocardial ischemia in patients with low- to intermediate-risk coronary artery disease, with significant positive and negative correlation in results. For patients who underwent confirmatory invasive angiography, the results of AI-CCTA and SPECT imaging were comparable. Future research focusing on prospective studies involving larger and more diverse patient populations is warranted to further investigate the benefits offered by AI-driven CCTA.

摘要

背景

采用冠状动脉计算机断层扫描血管造影(CCTA)和单光子发射计算机断层扫描(SPECT)进行的无创心脏检查正逐渐成为用于评估阻塞性冠状动脉疾病的侵入性血管造影的替代方法。我们旨在评估一种新型人工智能(AI)辅助的CCTA程序在缺血检测方面是否与SPECT成像相当。

方法

使用基于人工智能卷积神经网络机器学习的模型——动脉粥样硬化成像定量计算机断层扫描(AI-QCT)缺血模型对CCTA图像进行分析。共选取了183例患者(75例女性和108例男性,平均年龄60.8岁±12.3岁)。所有患者均接受了AI-QCT缺血增强的CCTA检查,其中60例同时接受了SPECT检查,16例接受了侵入性冠状动脉造影。由于数据不完整或冠状动脉异常,8项研究被排除在分析之外。

结果

共有175例患者(95%)进行了CCTA检查,被认为可接受AI-QCT缺血模型解读。与侵入性血管造影相比,AI-QCT缺血模型驱动的CCTA在预测冠状动脉缺血方面的敏感性为75%,特异性为70%,而SPECT分别为70%和53%。与SPECT相比,使用AI-QCT缺血模型时女性患者的阴性预测值较高(91%对68%,P = 0.042)。两种检查方法的受试者操作特征曲线下面积相似(AI-CCTA为0.81,SPECT为0.75,P = 0.526)。比较两种检查方法时,相关系数为r = 0.7(P < 0.04)。

结论

对于中低风险冠状动脉疾病患者,人工智能驱动的CCTA是检测心肌缺血的一种可行替代方法,结果具有显著的正相关和负相关。对于接受了确定性侵入性血管造影的患者,AI-CCTA和SPECT成像的结果相当。未来有必要开展针对更大规模、更多样化患者群体的前瞻性研究,以进一步探究人工智能驱动的CCTA所带来的益处。

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