Clinical Evaluation of the Automatic Coronary Artery Disease Reporting and Data System (CAD-RADS) in Coronary Computed Tomography Angiography Using Convolutional Neural Networks

Rationale and Objectives: The coronary artery disease reporting and data system (CAD-RADSTM) was recently introduced to standardise reporting. We aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADSTM in suspected coronary artery disease (CAD) patients.Materials and Methods: Clinical evaluation was performed in 346 patients who underwent coronary computed tomography angiography (CCTA). We compared deep learning (DL)-based CCTA with human readers for evaluation of CAD-RADSTM with com-mercially-available automated segmentation and manual postprocessing in a retrospective validation cohort.Results: Compared with invasive coronary angiography, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the DL model for diagnosis of CAD were 79.02%, 86.52%, 89.50%, 73.94%, and 82.08%, respectively. There was no sig-nificant difference between the DL-based and the reader-based CAD-RADSTM grading of CCTA results. Consistency testing showed that the Kappa value between the model and the readers was 0.775 (95% confidence interval [CI]: 0.728-0.823, p < 0.001), 0.802 (95% CI: 0.756-0.847, p < 0.001), and 0.796 (95% CI: 0.750-0.843, p < 0.001), respectively. This system reduces the time taken from 14.97 +/- 1.80 min to 5.02 +/- 0.8 min (p < 0.001).Conclusion: The standardised reporting of DL-based CAD-RADSTM in CCTA can accurately and rapidly evaluate suspected CAD patients, and has good consistency with grading by radiologists.

Automated summary

The study aimed to evaluate the utility of an automatic postprocessing and reporting system based on CAD-RADS in suspected coronary artery disease (CAD) patients. The DL model showed good consistency with human readers in diagnosing CAD, with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 79.02%, 86.52%, 89.50%, 73.94%, and 82.08% respectively. The system can accurately and rapidly evaluate suspected CAD patients and has good consistency with grading by radiologists.