Journal of cardiovascular computed tomography, 8 2 2025, Pages S1934-5925(25)00442-3 Incremental value of a CCTA-derived AI-based ischemia algorithm over standard CCTA interpretation of predicting myocardial ischemia in patients with suspected coronary artery disease. Nabeta T, Bär S, Maaniitty T, Kärpijoki H, Bax JJ, Saraste A, Knuuti J
Background
A novel artificial intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCTischemia) comprises a machine-learned method using atherosclerosis and vascular morphology features from coronary computed tomography angiography (CCTA) images to predict myocardial ischemia. This study evaluates the diagnostic performance of AI-QCTischemia compared to standard CCTA interpretation in detecting myocardial ischemia.
Methods and results
Patients with suspected coronary artery disease (CAD) undergoing CCTA were analyzed, with ischemia detected by stress [15O]H2O positron emission tomography (PET) as the reference. AI-QCTischemia analysis was successfully completed in 84 % of patients undergoing CCTA. A total of 1746 patients (mean age 62 ± 10 years, 44 % male) were included. In visual CCTA reading, 518 (30 %) patients had obstructive CAD, defined as diameter stenosis of ≥50 %. Myocardial ischemia on PET was detected in 325 (19 %) patients whereas AI-QCTischemia was positive in 430 (25 %) patients. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the AI-QCTischemia for the assessment of myocardial ischemia were 87 %, 81 %, 88 %, 61 %, and 95 %, respectively, compared to 86 %, 93 %, 85 %, 58 %, and 98 % for visual CCTA reading. AI-QCTischemia demonstrated higher diagnostic accuracy, specificity, and positive predictive value, but lower sensitivity and negative predictive value than visual CCTA reading (p-value <0.001). Combining AI-QCTischemia with visual CCTA reading improved ischemia discrimination compared with visual CCTA reading alone (area under the receiver operating characteristic curve 0.899 vs. 0.868, p < 0.001).
Conclusions
Among patients with suspected CAD, the AI-guided CCTA-derived ischemia algorithm demonstrated improved specificity as compared with visual CCTA reading but this was at a cost of decreased sensitivity, resulting in a slight improvement in diagnostic accuracy for predicting PET-defined myocardial ischemia. These findings suggest that AI-QCTischemia may support clinicians in refining diagnostic decision-making and streamlining patient selection for further testing.
