## 4 Discussion
The authors retrospectively evaluated aortic arch branching patterns on contrast-enhanced CT scans of the chest and neck of 474 individuals, 152 of them with and 322 without acute embolic stroke of the anterior cerebral circulation. They observed a significantly higher prevalence of the bovine aortic arch among stroke patients (25.7%) than that among controls (17.1%) (odds ratio 1.67); stroke patients were significantly more likely to have the CILCA 2 (10.5%) than controls (5.0%) (odds ratio 2.25), while the CILCA 1 variant was similarly common in both groups, showing the higher clinical relevance of detecting the CILCA 2 variant. In addition, in a cohort of 615 stroke patients [43], left laterality of cardioembolic stroke has been shown to be significantly more frequent (51.3%) in patients with the bovine arch variant than in patients with standard arch (43.6%), demonstrating that the arch anatomy influences stroke laterality and fostering further research into the causative hemodynamic factors. In this work, we have shown that the 3D Dense U-Net applied to chest CT is effective as an automatic segmentation tool on both the Center 1 and Center 2 datasets, with a similarity Dice coefficient of 0.916 and 0.912, respectively. With the refinement technique proposed, we also showed how focusing only on a portion of the volume to be segmented and zooming on it drastically improves the segmentation result on that region. A good segmentation in these final slices is a key factor in determining if a subject pertains to the CILCA category or not. Moreover, DenseNet-201 resulted in being the best model among three CNNs trained, supervised by a readers' consensus by visual inspection of the 2D axial and oblique images of the aortic arch. According to the classification performance obtained from an internal testing on 126 patients from the 2 centers, DenseNet-201 achieved ROC-AUC 87.0%, sensitivity 66.7%, specificity 90.5%, PPV 87.5%, NPV 73.1%, LR+ 7.0, and LR\(-\) 0.4. False-negative CILCA were found of type 1 in 42% of cases and of type 2 in 15% cases, respectively, proving that the system is less accurate when the geometrical variant is less evident. The relatively low sensitivity of our tool in the internal testing (66.7%) deserves a double comment. On the one hand, we should consider the subanalysis of the false-negative cases. The administration or the non-administration of contrast agent and the slice thickness reconstruction did not influence the false-negative rate, thus showing that datasets coming from unenhanced CT scans and obtained with relatively high reconstruction slice thickness are suitable for automatic analysis by our tool. Conversely, the significantly different proportion of false-negative CILCA 1 (42%) and CILCA 2 (15%) highlights that when the CILCA variant is anatomically more different from the non-CILCA pattern (i.e., it is a CILCA 2), the automatic tool reaches an 85% sensitivity, while in the cases of CILCA 1 the tool is more prone to miss the variant (sensitivity 58%). This difference could be related to the fact that the CILCA variants are the results of an embryologic anatomical continuum of the origin of the left carotid artery that moves from distal to proximal portions of the aortic arch, becoming CILCA 1 in the case of common origin with the innominate artery [40; 44]. So, the difference between a standard arch with a proximal origin from the arch, adjacent to the innominate artery, and a CILCA 1 variant with a very short common vessel may be very subtle and difficult to differentiate. On the other hand, we should consider that the CILCA variant is rarely reported in clinical practice: a retrospective preliminary analysis performed at Center 1 found a real-world low reporting rate of 2% in a series of unenhanced CT scans of patients without a history of aortic or supra-aortic vessel disease [45].