## 4 Discussion
Thus, a tool allowing to increase the detection rate to two-thirds of CILCA carriers would be useful for (i) increasing the reporting rate (taking into account an 87.5% PPV, which implies an acceptable rate of false positives to be checked by radiologists); (ii) attracting the attention of radiologists, especially those not dedicated to cardiovascular disease, on variants of the origin of supra-aortic vessels; (iii) allowing the implementation of prospective studies evaluating patients with bovine arch in terms of incidence of cardiovascular events, in particular aortic diseases and stroke. The main limitation of this study is a relatively small sample size, which implied relatively large 95% CIs for the performance metrics for the internal testing dataset. However, as discussed above, when compared with the heavy current under-reporting of CILCA variants on CT scans, the gain in detection allowed by our tool surely represents a step forward. The choice of using a deep-learning approach with dimensionality data reduction (from 3D mesh to 2D projection) was led by the specific geometrical nature of the CILCA anomaly, expressed in particular in the upper vessels. Considering that the anomaly can be identified by a human reader very well once looking at the 3D mesh projected on a 2D image, it was supposed that a deep learning-based approach could spot the 2D projected differences with a great improvement in the processing time. Our tool, in a fully-automatic process, takes only 2.5 min per patient for segmenting the aortic arch and upper vessels and classifying the presence or not of the CILCA variant. The possibility to use aortic arch-derived geometrical features to detect the CILCA variant deserves a separate note. Considering the relatively small number of subjects in our study, a traditional machine-learning approach using such features as input for classification could be a good compromise. However, while quite-standard geometric metrics are usually extracted from automatic geometrical models to assess and quantify geometric variants of the aortic arch, no standardized metrics are instead defined from the upper part of the aortic arch, including vessels. These are very important and simple metrics for the visual detection of CILCA. With this work, we wanted to mime the visual detection of radiologists of the CILCA variant, taking advantage of a full computer-vision-based tool in performing this task. In some studies, e.g., [34], deep learning has been applied on CT thoracic images for different purposes, including aorta segmentation, aortic disease detection, and risk stratification, demonstrating excellent correlations and adequate agreements compared with manual measurements for most segmented structures. A detailed analysis of these studies is out of the purpose of this work and can be found, for example, in [16; 17; 18; 46]. To the best of our knowledge, no AI application has yet been implemented for the automatic CILCA detection and classification of CT scans acquired for different clinical indications. A recent study [47] proposed a deep learning architecture feeding on high-resolution contrast-injected CT thoracic images and providing a comprehensive geometric analysis of the aorta for both standard and CILCA arch configurations, suitable to be used for unbiased quantitative analyses of geometric parameters in population studies for planning thoracic endovascular aortic repair. However, a single subject classifier based on the automatic classification of CILCA variants was not implemented, the above-mentioned method requiring instead the assessment of cut-off values for aortic metrics to define pathological versus normal patients. Moreover, slice thickness \(>\) 1.5 mm was excluded from the study, limiting the generalization of the developed geometric-variant detection model to CT scans acquired for different clinical indications than aortic assessment.