## 4 Discussion  
The main result of this work is the development of an AI tool applied to chest CT scans, enabling the detection of CILCA anatomical variants based on a two-step deep learning procedure. First, a fully automatic semantic segmentation procedure of CT images to extract the aortic arch and the origin of supra-aortic vessels; second, a fully automatic binary classification procedure into the two classes of cases with CILCA or cases without CILCA. In other words, this AI system suggests the presence of a CILCA variant without time-consuming human intervention related to image-specific reconstruction or segmentation. The clinical value of this approach can be appreciated when considering some epidemiological and clinical implications of the CILCA anatomy variant. As pointed out in the Introduction, the prevalence of CILCA has been reported to vary according to different studies [7, 11, 12, 13], probably meaning that about 1 out of 5-6 patients might have this anatomical variant. Of note, CILCA is rarely noticed and reported when the CT scan is not a \"cardiovascular\" examination or specifically aimed at evaluating the aortic arch and supra-aortic vessels. The emergent evidence of CILCA as a risk factor for aortic disease and stroke highlights the potential usefulness of a tool proposing an automatic CILCA detection in a substantial proportion of patients with this anatomical variant by a simple automatic reading of a common CT chest scan. Several groups of authors reported the association between aortic arch variants and thoracic aortic disease. A systematic review and meta-analysis published in 2020 by [PERSON] et al. [8], including 8 studies enrolling 11,381 subjects, found the rate of thoracic aortic disease among CILCA arch patients to be higher (41.5%, 95% CI 28.1-56.4) than the rate among patients with standard arch configuration (34.0%, 20.1-51.4), with an odds ratio of 1.4 and an overall significant association between CILCA arch and thoracic aortic disease. More recently, [PERSON] et al. [38] investigated the prevalence of aortic arch variants in the adult population and their association with thoracic aortic aneurysm disease. On a single-institution large database of 21,336 CT scans, they found 603 (2.8%) described arch anomalies, 354 of them (58.7%) with the bovine arch being the most common diagnosis. Although this low rate of arch anomalies opens the previous discussion about underreporting, their results are interesting. Patients with arch anomalies were significantly more likely to be female, non-Caucasian, and hypertensive; the prevalence of thoracic aortic aneurysms in the arch anomalies group was 10.8%, significantly higher than 4.1% in the non-variant cohort. Patients with bovine arch showed the second highest prevalence of thoracic aneurysm (13%) after those with right-sided arch combined with aberrant left subclavian configuration (33%) and before those with aberrant right subclavian artery (8.2%). A significantly higher prevalence of bovine arch was also observed by [PERSON] et al. [39] in male patients undergoing blunt isthmic aortic trauma repair (57.6%) versus controls (34.8%), playing in favor of a role of bovine arch in facilitating trauma effects on the aorta. Finally, children with bovine arch anatomy and coarctation are at a significantly higher risk of re-coarctation following coarctation repair [40]. The association between aortic arch variants and thoracic aortic diseases depends on differences in hemodynamic flow patterns, and four-dimensional flow magnetic resonance imaging preliminary data corroborate this hypothesis [9]. While no association between variant aortic arch branching pattern and intracranial aneurysms was found by [PERSON] et al. [41], a significant association between CILCA variant and stroke was found by [PERSON] et al. [42], who defined the bovine aortic arch as a \"biomarker for embolic stroke\".