Coronary artery disease detection using deep learning and ultrahigh-resolution photon-counting coronary CT angiography.

Autor: Brendel JM; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Walterspiel J; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Hagen F; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Kübler J; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Brendlin AS; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Afat S; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Paul JF; Institut Mutualiste Montsouris, Department of Radiology, Cardiac Imaging, 75014 Paris, France; Spimed-AI, 75014 Paris, France., Küstner T; Department of Radiology, Diagnostic and Interventional Radiology, Medical Image and Data Analysis (MIDAS.lab), University of Tübingen, 72076, Germany., Nikolaou K; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany., Gawaz M; Department of Internal Medicine III, Cardiology and Angiology, University of Tübingen, 72076, Germany., Greulich S; Department of Internal Medicine III, Cardiology and Angiology, University of Tübingen, 72076, Germany., Krumm P; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany. Electronic address: patrick.krumm@uni-tuebingen.de., Winkelmann MT; Department of Radiology, Diagnostic and Interventional Radiology, University of Tübingen, 72076, Germany.
Jazyk: angličtina
Zdroj: Diagnostic and interventional imaging [Diagn Interv Imaging] 2024 Oct 03. Date of Electronic Publication: 2024 Oct 03.
DOI: 10.1016/j.diii.2024.09.012
Abstrakt: 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.
Competing Interests: Declaration of competing interest Jean-François Paul is co-founder of Spimed-AI.
(Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)
Databáze: MEDLINE
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