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.
Authors
Affiliations (7)
Affiliations (7)
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands.
- Turku PET Centre, Turku University Hospital and University of Turku, Finland; Department of Cardiology, Bern University Hospital Inselspital, Bern, Switzerland.
- Turku PET Centre, Turku University Hospital and University of Turku, Finland; Department of Clinical Physiology, Nuclear Medicine and PET, Turku University Hospital, Turku, Finland.
- Turku PET Centre, Turku University Hospital and University of Turku, Finland.
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands; Heart Center, Turku University Hospital and University of Turku, Turku, Finland; Faculty of Medicine, University of Turku, Turku, Finland.
- Turku PET Centre, Turku University Hospital and University of Turku, Finland; Heart Center, Turku University Hospital and University of Turku, Turku, Finland; Faculty of Medicine, University of Turku, Turku, Finland.
- Department of Cardiology, Leiden University Medical Center, Leiden, the Netherlands; Turku PET Centre, Turku University Hospital and University of Turku, Finland; Department of Clinical Physiology, Nuclear Medicine and PET, Turku University Hospital, Turku, Finland; Faculty of Medicine, University of Turku, Turku, Finland. Electronic address: [email protected].
Abstract
A novel artificial intelligence-guided quantitative computed tomography ischemia algorithm (AI-QCT<sub>ischemia</sub>) 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<sub>ischemia</sub> compared to standard CCTA interpretation in detecting myocardial ischemia. Patients with suspected coronary artery disease (CAD) undergoing CCTA were analyzed, with ischemia detected by stress [<sup>15</sup>O]H<sub>2</sub>O positron emission tomography (PET) as the reference. AI-QCT<sub>ischemia</sub> 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<sub>ischemia</sub> was positive in 430 (25 %) patients. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the AI-QCT<sub>ischemia</sub> 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<sub>ischemia</sub> 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<sub>ischemia</sub> 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). 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<sub>ischemia</sub> may support clinicians in refining diagnostic decision-making and streamlining patient selection for further testing.