Jo Jung In, Koo Hyun Jung, Kang Joon Won, Kim Young Hak, Yang Dong Hyun
Department of Radiology, National Medical Center, Seoul, South Korea.
Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Am J Cardiol. 2025 Mar 15;239:82-89. doi: 10.1016/j.amjcard.2024.12.011. Epub 2024 Dec 11.
We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary computed tomography angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR). This single-center retrospective study included 195 symptomatic patients (mean age 61 ± 10 years, 149 men, 585 coronary arteries) with 215 intermediate coronary lesions, with quantitative coronary angiography (QCA) diameter stenosis ranging from 20% to 80%. An AI-driven research prototype (AI-CCTA) was used to quantify stenosis on CCTA images. The diagnostic accuracy of AI-CCTA was assessed on a per-vessel basis using ICA stenosis grading (with ≥50% stenosis) or invasive FFR (≤0.80) as reference standards. AI-driven diameter stenosis was correlated with the QCA results and expert manual measurements subsequently. The disease prevalence in the 585 coronary arteries, as determined by invasive angiography (≥50%), was 46.5%. AI-CCTA exhibited sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) of 71.7%, 89.8%, 85.9%, 78.5%, and 0.81, respectively. The diagnostic performance of AI-CCTA was moderate for the 215 intermediate lesions assessed using QCA and FFR, with an AUC of 0.63 for QCA and FFR. AI-CCTA demonstrated a moderate correlation with QCA (r = 0.42, p <0.001) for measuring the degree of stenosis, which was notably better than the results from manual quantification versus QCA (r = 0.26, p = 0.001). In conclusion, AI-driven CCTA analysis exhibited promising results. AI-CCTA demonstrated a moderate relation with QCA in intermediate coronary stenosis lesions; however, its results surpassed those of manual evaluations.
我们旨在比较基于人工智能（AI）的冠状动脉计算机断层扫描血管造影（CCTA）对冠状动脉狭窄的评估与有创冠状动脉造影（ICA）及其定量对应物有创血流储备分数（FFR）的评估。这项单中心回顾性研究纳入了195例有症状患者（平均年龄61±10岁，男性149例，585支冠状动脉），这些患者有215处中度冠状动脉病变，定量冠状动脉造影（QCA）直径狭窄范围为20%至80%。使用一个由人工智能驱动的研究原型（AI-CCTA）对CCTA图像上的狭窄进行量化。以ICA狭窄分级（狭窄≥50%）或有创FFR（≤0.80）作为参考标准，在每支血管的基础上评估AI-CCTA的诊断准确性。随后将人工智能驱动的直径狭窄与QCA结果及专家手动测量结果进行关联。通过有创血管造影确定（≥50%），585支冠状动脉中的疾病患病率为46.5%。AI-CCTA的敏感性、特异性、阳性预测值、阴性预测值和曲线下面积（AUC）分别为71.7%、89.8%、85.9%、78.5%和0.81。对于使用QCA和FFR评估的215处中度病变，AI-CCTA的诊断性能中等，QCA和FFR的AUC为0.63。在测量狭窄程度方面，AI-CCTA与QCA显示出中度相关性（r = 0.42，p <0.001），这明显优于手动量化与QCA的结果（r = 0.26，p = 0.001）。总之，人工智能驱动的CCTA分析显示出有前景的结果。在中度冠状动脉狭窄病变中，AI-CCTA与QCA显示出中度相关性；然而，其结果优于手动评估。
