## 3 Results
### Training and Testing of the Models  
The automatic segmentation process realized by the 3D Dense U-Net tested on CTs from Center 1, when compared with the manual segmentation as the reference standard, showed a mean Dice score of 0.904 (95% CI 0.899-0.909), which improved to 0.916 (95% CI 0.911-0.922) after applying the refinement technique, consisting of focusing only on a portion of the volume to be segmented and zooming in on it. In particular, for the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **CILCA (_n_ = 100)** & **Non-CILCA (_n_ = 100)** \\ \hline Median age, years (IQR) & 70 (64–76) & 83 (79–86) \\ Males, \(n\) (\%) & 58 (58) & 39 (39) \\ Contrast-enhanced studies, \(n\) (\%) & 82 (82) & 80 (80) \\ Slice thickness reconstruction, _(n)_ & & \\
1 mm & 21 & 67 \\
1.5 mm & 40 & 1 \\
3 mm & 6 & 8 \\
5 mm & 33 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Patients’ demographics, number of contrast-enhanced studies, and slice thickness reconstruction at Center 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **CILCA (_n_ = 23)** & **Non-CILCA (_n_ = 79)** \\ \hline Median age, years (IQR) & 65 (57–70) & 66 (54–74) \\ Males, \(n\) ( \%) & 14 (61) & 52 (66) \\ Contrast-enhanced studies, \(n\) (\%) & 23 (100) & 78 (99) \\ Slice thickness reconstruction, _(n)_ & & \\
1 mm & 0 & 3 \\
1.5 mm & 2 & 4 \\
3 mm & 21 & 72 \\
5 mm & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Patients’ demographics, number of contrast-enhanced studies, and slice thickness reconstruction at Center 2.  
supra-aortic arteries, the Dice score of images from Center 1 improved from 0.604 (95% CI 0.569-0.639) to 0.771 (95% CI 0.757-0.786). For the CTs from Center 2, the Dice score was 0.907 (95% CI 0.903-0.912), which improved to 0.912 (95% CI 0.906-0.917) after refinement. In particular, in the upper arteries, the Dice score from Center 2 improved from 0.714 (95% CI 0.683-0.744) to 0.792 (95% CI 0.778-0.806). Table 3 deepens the analysis of the results identifying 2 different subgroups: contrast-enhanced CTs vs. non-enhanced CTs and CT slice thickness \(\leq\) 1.5 mm vs. CT slice thickness \(>\) 1.5 mm. This analysis was conducted only on Center 1 data since the cardinality of data in these subgroups was not relevant in Center 2 data. The table reports the Dice performances before and after the refinement operation. Statistical comparison (two-sample \(t\)-test) showed significantly better segmentation results for images with contrast media injection and slice thickness lower than 1.5 mm (\(p<\) 0.005 for both gross and refined Dice scores), as expected due to the higher contrast and spatial resolution of images. Figure 1 shows 3 representative examples (A, B, and C) of the automatic segmentation process realized by the 3D Dense U-Net when the Dice coefficient was \(>\)0.94, \(>\)0.91, and \(>\)0.90, respectively, both for the first prediction and the refined prediction compared with ground truth. Figure 1: Representative examples (rows A, B, and C) of the automatic segmentation process realized by the 3D Dense U-Net when the Dice coefficient was \(>\)0.94 (row A), \(>\)0.91 (row B), and \(>\)0.90 (row C), both for the first prediction (**A1–C1**) and the refined prediction (**A2–C2**) compared with ground truth, i.e., manual segmentation (**A3–C3**). The procedure of classifying CILCA versus NON-CILCA cases showed different performances for the training (5-fold cross-validation) and the internal independent testing, depending on the choice of the model architecture, as reported in Table 4. In particular, the best model, i.e., DenseNet201, obtained for the internal independent testing a sensitivity of 66.7% (42/63) associated with a specificity of 90.5% (57/63), a PPV of 87.5% (42/48) and a NPV of 73.1% (57/78). The initial memory size of each CNN was around 96 MB, 5 MB, and 77 MB for ResNet-50, SqueezeNet, and DenseNet-201, respectively.