Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography - 18/02/25
, Moritz T. Winkelmann a
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Highlights |
• | 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. |
Abstract |
Purpose |
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).
Materials and methods |
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.
Results |
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.
Conclusion |
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.
Le texte complet de cet article est disponible en PDF.Keywords : Computed tomography, Coronary artery disease, Deep learning, Photon-counting CT, Ultrahigh resolution
Abbreviations : AI, AUC, CAD, CAD-RADS, CCTA, CDTI vol , CI, DLP, FFR, FFRai, PC, PC-CCTA, PPV, NPV, SD, UHR
Plan
Vol 106 - N° 2
P. 68-75 - février 2025 Retour au numéro
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