Abstract
The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.
Highlights
Thoracic aortic pathologies, in particular aortic aneurysm and dissection, are among the main cardiovascular causes of morbidity and mortality [1]
Among aortic arch variants, we take here into consideration the so-called “bovine” arch, a configuration characterized by a common origin of the innominate and left carotid artery (CILCA), including the case of left carotid artery sharing a common orifice and a common trunk with the innominate artery (CILCA type 1) and the case of the left carotid artery branching off from the innominate artery (CILCA type 2) [5]
Dumfarth et al [6] reported that 556 patients with thoracic aortic diseases showed a significantly higher prevalence of a CILCA variant (24.6%) than that seen among 4617 historical controls (14.0%)
Summary
In particular aortic aneurysm and dissection, are among the main cardiovascular causes of morbidity and mortality [1]. Dumfarth et al [6] reported that 556 patients with thoracic aortic diseases (aneurysms, dissections, intramural hematoma, or aortic rupture) showed a significantly higher prevalence of a CILCA variant (24.6%) than that seen among 4617 historical controls (14.0%). Shalhub et al [9] reported a significant general association between aortic arch variants and type B aortic dissections, with patients with CILCA accounting for 33.5% of cases This increase in the likelihood of thoracic aortic disease has been related to alterations in blood flow caused by the CILCA, generating areas of accelerated flow and heightened wall shear stress [9], which are linked to endothelial damage and arterial injury [10]. An early identification of patients with CILCA may be beneficial as it could allow the implementation of tailored surveillance protocols in order to avert unexpected disease diagnoses and potential adverse events
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