Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection |
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| Institution: | 1. Institut Mutualiste Montsouris, Department of Radiology, Cardiac Imaging, 75014 Paris, France;2. Spimed-AI, 92340 Bourg-La-Reine, France;3. CHU Rouen, Department of Biostatistics, 76000 Rouen, France;1. Department of Radiology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Science, 2-5-1 Shikata-cho kita-ku, Okayama 700-8558, Japan;2. Department of Radiological Technology, Okayama University Graduate School of Health Sciences, Okayama 700-8558, Japan;1. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina;2. Institute of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim-Heidelberg University, Mannheim, Germany;3. Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, Georgia;4. Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, South Carolina;5. Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany;6. Center for Medical Imaging North East Netherlands, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands;7. Department of Radiological Sciences, Oncology and Pathology, University of Rome “Sapienza”, Rome, Italy;8. Kardiologie MVZ München-Nord, Munich, Germany;9. Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany;10. Department of Internal Medicine, St. Johannes-Hospital, Dortmund, Germany;1. School of Mathmatics, Shandong University, Jinan, Shandong 250100, China;2. Shandong Provincial Key Laboratory of Digital Media Technology, Jinan, Shandong 250014, China;3. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, Shandong 250014, China;4. School of Physics, Shandong University, Jinan, Shandong 250100, China;5. Department of Cerebrovascular Diseases, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, China;6. School of Computer Science and Technology, Shandong University, Jinan, Shandong 250101, China |
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| Abstract: | PurposeThe purpose of this study was to evaluate a deep-learning model (DLM) for classifying coronary arteries on coronary computed tomography -angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS).Materials and methodsThe DLM was trained with 10,800 curved multiplanar reformatted (cMPR) CCTA images classified by an expert radiologist using the CAD-RADS. For each of the three main coronary arteries, nine cMPR images 40° apart acquired around each arterial circumference were then classified by the DLM using the highest probability. For the validation set composed of 159 arteries from 53 consecutive patients, the images were read by two senior and two junior readers; consensus of the two seniors was the reference standard. With the DLM, the majority vote for the nine images was used to classify each artery. Three groups (CAD-RADS 0, 1–2, or 3–4–5) and 2 groups CAD-RADS 0–1–2 or 3–4–5 (<50% vs. ≥50% stenosis) were used for comparisons with readers and consensus. Performance of the model and readers was compared to the consensus reading using the intraclass coefficient (ICC) and Cohen's kappa coefficient at the artery and patient levels.ResultsWith the three groups at the artery level, the ICC of the DLM was 0.82 (95% CI: 0.75–0.88) and not significantly different from those of 3/4 readers; accuracy was 81%. With the binary classification, Cohen kappa coefficient of the DLM was 0.85 (95% CI: 0.69–0.94) and not significantly different from that of 3/4 readers; accuracy was 96%. At the patient level, sensitivity and specificity were 93% and 97% respectively, and the negative predictive value was 97%.ConclusionThe DLM detected ≥50% stenoses with performances similar to those achieved by senior radiologists. |
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