Does automated deep learning accurately detect significant coronary artery disease on non-UHR PC-CCTA compared to human expert assessment on UHR PC-CCTA in patients with suspected CAD?
Automated deep learning demonstrates high diagnostic accuracy for detecting significant coronary artery disease on non-ultra-high resolution photon-counting CT, supporting its potential as a pre-reading tool.
• Artificial intelligence shows remarkable performance in detecting significant coronary artery disease on ultrahigh-resolution photon-counting CT. • Artificial intelligence may serve as a valuable pre-reading tool to assist human readers for coronary artery disease detection on photon-counting coronary CT angiography in daily clinical practice. • Integrating artificial intelligence for coronary artery disease detection could improve workflow efficiency and help radiologists manage increasing workloads. The purpose of this study was to evaluate the diagnostic performance of automated deep learning in the detection of coronary artery disease (CAD) on photon-counting coronary CT angiography (PC-CCTA). Consecutive patients with suspected CAD who underwent PC-CCTA between January 2022 and December 2023 were included in this retrospective, single-center study. Non-ultra-high resolution (UHR) PC-CCTA images were analyzed by artificial intelligence using two deep learning models (CorEx, Spimed-AI), and compared to human expert reader assessment using UHR PC-CCTA images. Diagnostic performance for global CAD assessment (at least one significant stenosis ≥ 50 %) was estimated at patient and vessel levels. A total of 140 patients (96 men, 44 women) with a median age of 60 years (first quartile, 51; third quartile, 68) were evaluated. Significant CAD on UHR PC-CCTA was present in 36/140 patients (25.7 %). The sensitivity, specificity, accuracy, positive predictive value), and negative predictive value of deep learning-based CAD were 97.2 %, 81.7 %, 85.7 %, 64.8 %, and 98.9 %, respectively, at the patient level and 96.6 %, 86.7 %, 88.1 %, 53.8 %, and 99.4 %, respectively, at the vessel level. The area under the receiver operating characteristic curve was 0.90 (95 % CI: 0.83–0.94) at the patient level and 0.92 (95 % CI: 0.89–0.94) at the vessel level. Automated deep learning shows remarkable performance for the diagnosis of significant CAD on non-UHR PC-CCTA images. AI pre-reading may be of supportive value to the human reader in daily clinical practice to target and validate coronary artery stenosis using UHR PC-CCTA.
Brendel et al. (Fri,) studied this question.
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