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
Abstract
Background: A novel artificial intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCT(ischemia)) 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-QCT(ischemia) 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 [O-15]H2O positron emission tomography (PET) as the reference. AI-QCT(ischemia) 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-QCT(ischemia) was positive in 430 (25 %) patients. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the AI-QCT(ischemia) 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-QCT(ischemia) 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-QCT(ischemia) 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-QCT(ischemia) may support clinicians in refining diagnostic decision-making and streamlining patient selection for further testing.
