Performance of a deep learning algorithm for the evaluation of CAD-RADS classification with CCTA

Giuseppe Muscogiuri, Mattia Chiesa, Michela Trotta, Marco Gatti, Vitanio Palmisano, Serena Dell'Aversana, Francesca Baessato, Annachiara Cavaliere, Gloria Cicala, Antonella Loffreno, Giulia Rizzon, Marco Guglielmo, Andrea Baggiano, Laura Fusini, Luca Saba, Daniele Andreini, Mauro Pepi, Mark G. Rabbat, Andrea I. Guaricci, Carlo N. De CeccoGualtiero Colombo, Gianluca Pontone

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)25-32
Number of pages8
JournalAtherosclerosis
Volume294
DOIs
Publication statusPublished - Feb 2020

Keywords

  • Artificial intelligence
  • CADRADS
  • Convolutional neural network
  • Coronary artery disease
  • Plaque characterization

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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