Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA. (February 2020)

• Record Type: Journal Article
• Authors: Muscogiuri, Giuseppe; Chiesa, Mattia; Trotta, Michela; Gatti, Marco; Palmisano, Vitanio; Dell'Aversana, Serena; Baessato, Francesca; Cavaliere, Annachiara; Cicala, Gloria; Loffreno, Antonella; Rizzon, Giulia; Guglielmo, Marco; Baggiano, Andrea; Fusini, Laura; Saba, Luca; Andreini, Daniele; Pepi, Mauro; Rabbat, Mark G.; Guaricci, Andrea I.; De Cecco, Carlo N.; Colombo, Gualtiero; Pontone, Gianluca
• Is Part Of: Atherosclerosis. Volume 294(2020)
• Page Start: 25
• Page End: 32
• Publication Date: 2020-02
• Subjects: CADRADS -- Convolutional neural network -- Artificial intelligence -- Coronary artery disease -- Plaque characterization; Arteriosclerosis -- Periodicals; Electronic journals; 616.136
• DOI: 10.1016/j.atherosclerosis.2019.12.001
• Languages: English
• ISSNs: 0021-9150

Abstract

Abstract: Background and aims: Artificial intelligence (AI) is increasing its role in diagnosis of patients with suspicious coronary artery disease. The aim of this manuscript is to develop a deep convolutional neural network (CNN) to classify coronary computed tomography angiography (CCTA) in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category. Methods: Two hundred eighty eight patients who underwent clinically indicated CCTA were included in this single-center retrospective study. The CCTAs were stratified by CAD-RADS scores by expert readers and considered as reference standard. A deep CNN was designed and tested on the CCTA dataset and compared to on-site reading. The deep CNN analyzed the diagnostic accuracy of the following three Models based on CAD-RADS classification: Model A (CAD-RADS 0 vs CAD-RADS 1–2 vs CAD-RADS 3, 4, 5), Model 1 (CAD-RADS 0 vs CAD-RADS>0), Model 2 (CAD-RADS 0–2 vs CAD-RADS 3–5). Time of analysis for both physicians and CNN were recorded. Results: Model A showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 47%, 74%, 77%, 46% and 60%, respectively. Model 1 showed a sensitivity, specificity, negative predictive value, positive predictive value and accuracy of 66%, 91%, 92%, 63%, 86%, respectively. Conversely, Model 2 demonstrated the following sensitivity, specificity, negative predictive value, positive predictive value and accuracy: 82%, 58%, 74%, 69%, 71%, respectively. Time of analysis was significantly lower using CNN as compared to on-site reading (530.5 ± 179.1 vs 104.3 ± 1.4 sec, p =0.01) Conclusions: Deep CNN yielded accurate automated classification of patients with CAD-RADS. Graphical abstract: Image 1 Highlights: Deep convolutional neural network (CNN) yielded accurate automated Coronary Artery Disease Reporting and Data System (CAD-RADS) classification in patients with suspicious CAD. CAD-RADS classification is significantly faster compared to human evaluation. CNN can reduce the time of CCTA reporting in the next future.