The Role of Artificial Intelligence in Coronary Calcium Scoring in Standard Cardiac Computed Tomography and Chest Computed Tomography With Different Reconstruction Kernels

Purpose: To assess the correlation of coronary calcium score (CS) obtained by artificial intelligence (AI) with those obtained by electrocardiography gated standard cardiac computed tomography (CCT) and nongated chest computed tomography (ChCT) with different reconstruction kernels. Patients and Methods: Seventy-six patients received standard CCT and ChCT simultaneously. We compared CS obtained in 4 groups: CSCCT, by the traditional method from standard CCT, 25 cm field of view, 3 mm slice thickness, and kernel filter convolution 12 (FC12); CSAICCT, by AI from the standard CCT; CSChCTsoft, by AI from the non-gated CCT, 40 cm field of view, 3 mm slice thickness, and a soft kernel FC02; and CSChCTsharp, by AI from CCT image with same parameters for CSChCTsoft except for using a sharp kernel FC56. Statistical analyses included Spearman rank correlation coefficient (rho), intraclass correlation (ICC), Bland-Altman plots, and weighted kappa analysis (kappa). Results: The CSAICCT was consistent with CSCCT (rho = 0.994 and ICC of 1.00, P < 0.001) with excellent agreement with respect to cardiovascular (CV) risk categories of the Agatston score (kappa = 1.000). The correlation between CSChCTsoft and CSChCTsharp was good (rho = 0.912, 0.963 and ICC = 0.929, 0.948, respectively, P < 0.001) with a tendency of underestimation (Bland-Altman mean difference and 95% upper and lower limits of agreements were 329.1 [-798.9 to 1457] and 335.3 [-651.9 to 1322], respectively). The CV risk category agreement between CSChCTsoft and CSChCTsharp was moderate (kappa = 0.556 and 0.537, respectively). Conclusions: There was an excellent correlation between CSCCT and CSAICCT, with excellent agreement between CV risk categories. There was also a good correlation between CSCCT and CS obtained by ChCT albeit with a tendency for underestimation and moderate accuracy in terms of CV risk assessment.