Kübler Jens, Brendel Jan M, Küstner Thomas, Walterspiel Jonathan, Hagen Florian, Paul Jean-François, Nikolaou Konstantin, Gassenmaier Sebastian, Tsiflikas Ilias, Burgstahler Christof, Greulich Simon, Winkelmann Moritz T, Krumm Patrick
Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Tübingen, Germany.
Department of Radiology, Institut Mutualiste Montsouris, Cardiac Imaging, 75014, Paris, France.
Int J Cardiovasc Imaging. 2024 Dec;40(12):2503-2511. doi: 10.1007/s10554-024-03256-y. Epub 2024 Oct 7.
This study evaluates the diagnostic performance of artificial intelligence (AI)-based coronary computed tomography angiography (CCTA) for detecting coronary artery disease (CAD) and assessing fractional flow reserve (FFR) in asymptomatic male marathon runners.
We prospectively recruited 100 asymptomatic male marathon runners over the age of 45 for CAD screening. CCTA was analyzed using AI models (CorEx and Spimed-AI) on a local server. The models focused on detecting significant CAD (≥ 50% diameter stenosis, CAD-RADS 3, 4, or 5) and distinguishing hemodynamically significant stenosis (FFR ≤ 0.8) from non-significant stenosis (FFR > 0.8). Statistical analysis included sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.
The AI model demonstrated high sensitivity, with 91.2% for any CAD and 100% for significant CAD, and high NPV, with 92.7% for any CAD and 100% for significant CAD. The diagnostic accuracy was 73.4% for any CAD and 90.4% for significant CAD. However, the PPV was lower, particularly for significant CAD (25.0%), indicating a higher incidence of false positives.
AI-enhanced CCTA is a valuable non-invasive tool for detecting CAD in asymptomatic, low-risk populations. The AI model exhibited high sensitivity and NPV, particularly for identifying significant stenosis, reinforcing its potential role in screening. However, limitations such as a lower PPV and overestimation of disease indicate that further refinement of AI algorithms is needed to improve specificity. Despite these challenges, AI-based CCTA offers significant promise when integrated with clinical expertise, enhancing diagnostic accuracy and guiding patient management in low-risk groups.
本研究评估基于人工智能（AI）的冠状动脉计算机断层扫描血管造影（CCTA）在检测无症状男性马拉松运动员冠状动脉疾病（CAD）及评估血流储备分数（FFR）方面的诊断性能。
我们前瞻性招募了100名年龄超过45岁的无症状男性马拉松运动员进行CAD筛查。在本地服务器上使用AI模型（CorEx和Spimed-AI）对CCTA进行分析。这些模型专注于检测显著CAD（直径狭窄≥50%，CAD-RADS 3、4或5），并区分血流动力学显著狭窄（FFR≤0.8）与非显著狭窄（FFR>0.8）。统计分析包括敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性。
AI模型显示出高敏感性，任何CAD的敏感性为91.2%，显著CAD的敏感性为100%，且NPV高，任何CAD的NPV为92.7%，显著CAD的NPV为100%。任何CAD的诊断准确性为73.4%，显著CAD的诊断准确性为90.4%。然而，PPV较低，尤其是显著CAD的PPV为25.0%，表明假阳性发生率较高。
AI增强的CCTA是检测无症状、低风险人群CAD的有价值的非侵入性工具。AI模型表现出高敏感性和NPV，特别是在识别显著狭窄方面，强化了其在筛查中的潜在作用。然而，诸如较低的PPV和疾病高估等局限性表明，需要进一步优化AI算法以提高特异性。尽管存在这些挑战，但基于AI的CCTA与临床专业知识相结合时具有显著前景，可提高诊断准确性并指导低风险人群的患者管理。
