Artificial intelligence-enhanced detection of subclinical coronary artery disease in athletes: diagnostic performance and limitations.

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.

Kübler J ，Brendel JM ，Küstner T ，Walterspiel J ，Hagen F ，Paul JF ，Nikolaou K ，Gassenmaier S ，Tsiflikas I ，Burgstahler C ，Greulich S ，Winkelmann MT ，Krumm P ... -  《-》

Invasive fractional-flow-reserve prediction by coronary CT angiography using artificial intelligence vs. computational fluid dynamics software in intermediate-grade stenosis.

Coronary computed angiography (CCTA) with non-invasive fractional flow reserve (FFR) calculates lesion-specific ischemia when compared with invasive FFR and can be considered for patients with stable chest pain and intermediate-grade stenoses according to recent guidelines. The objective of this study was to compare a new CCTA-based artificial-intelligence deep-learning model for FFR prediction (FFRAI) to computational fluid dynamics CT-derived FFR (FFRCT) in patients with intermediate-grade coronary stenoses with FFR as reference standard. The FFRAI model was trained with curved multiplanar-reconstruction CCTA images of 500 stenotic vessels in 413 patients, using FFR measurements as the ground truth. We included 37 patients with 39 intermediate-grade stenoses on CCTA and invasive coronary angiography, and with FFRCT and FFR measurements in this retrospective proof of concept study. FFRAI was compared with FFRCT regarding the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy for predicting FFR ≤ 0.80. Sensitivity, specificity, PPV, NPV, and diagnostic accuracy of FFRAI in predicting FFR ≤ 0.80 were 91% (10/11), 82% (23/28), 67% (10/15), 96% (23/24), and 85% (33/39), respectively. Corresponding values for FFRCT were 82% (9/11), 75% (21/28), 56% (9/16), 91% (21/23), and 77% (30/39), respectively. Diagnostic accuracy did not differ significantly between FFRAI and FFRCT (p = 0.12). FFRAI performed similarly to FFRCT for predicting intermediate-grade coronary stenoses with FFR ≤ 0.80. These findings suggest FFRAI as a potential non-invasive imaging tool for guiding therapeutic management in these stenoses.

Peters B ，Paul JF ，Symons R ，Franssen WMA ，Nchimi A ，Ghekiere O ... -  《-》

Enhancing coronary artery plaque analysis via artificial intelligence-driven cardiovascular computed tomography.

Xia J ，Bachour K ，Suleiman AM ，Roberts JS ，Sayed S ，Cho GW ... -  《-》

The value of fractional flow reserve based on coronary computed tomography angiography with low-dose contrast agent in noninvasive diagnosis of coronary artery disease.

The study aimed, using invasive coronary angiography (ICA) as the gold standard, to investigate the noninvasive diagnostic value of flow reserve fraction derived from coronary computed tomography angiography (CCTA) with low-dose contrast agent in coronary artery disease (CAD). A total of 163 patients with clinical symptoms related to CAD were enrolled between 1 January 2022 and 30 January 2023. The patients received CCTA with a low dose of contrast agent to rule out CAD. If significant (CCTA ≥ 50%) stenosis is suspected, ICA is performed to further evaluate the CCTA for coronary lesions. CT-FFR is calculated from the CCTA dataset using a machine learning-based algorithm. Compared with ICA as a reference standard, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of low-dose contrast agent CT-FFR in the diagnosis of myocardial ischemia were calculated. Coronary lesions with CT-FFR ≤0.80 were defined as hemodynamically significant. Obstructive CAD was excluded by low-contrast CCTA in 87 of 163 patients (53.7%). In the remaining 75 patients (42.35%), at least one coronary artery stenosis was greater than 50%. The accuracy, sensitivity, specificity, PPV, and NPV of low-dose contrast agent CT-FFR on a patient-based evaluation in diagnosing CAD were 93.06, 93.44, 90.01, 98.28, and 71.43%, respectively (Kappa = 0.759). The accuracy, sensitivity, specificity, PPV, and NPV of low-dose contrast agent CT-FFR on a vessel-based evaluation in diagnosing CAD were 86.72, 82.76, 91.58, 92.31, and 81.31%, respectively (Kappa = 0.735). Pearson correlation analysis showed that the ICA examination had a good correlation with CT-FFR value of low-dose contrast media ( r  = 0.731, P  < 0.01). Moreover, in 81.31% of cases, additional analysis of CT-FFR correctly excluded the hemodynamic significance of stenosis. CT-FFR based on low-dose contrast agent CCTA is a very promising noninvasive approach to exclude hemodynamically significant coronary artery stenosis in patients with suspected coronary heart disease while reducing renal burden and helping to reduce the rate of ICA in this high-risk population.

Wang Z ，Luan X ，Zhang Q ，Chu C ，Xu X ，Chai H ，Song P ... -  《-》

On-Site Computed Tomography-Derived Fractional Flow Reserve Using a Machine-Learning Algorithm　- Clinical Effectiveness in a Retrospective Multicenter Cohort.

Kurata A ，Fukuyama N ，Hirai K ，Kawaguchi N ，Tanabe Y ，Okayama H ，Shigemi S ，Watanabe K ，Uetani T ，Ikeda S ，Inaba S ，Kido T ，Itoh T ，Mochizuki T ... -  《-》