## 3 Materials and Methods
### Dataset Generation and Annotation  
All CBCTs were obtained using a NewTom/NTVGiMK4 machine (3 mA, 110 kV, 0.3 mm cubic voxels) (QR, Verona, Italy). The Affidea center in Modena (Italy) took care of the acquisition and export of the images in DICOM format. The scanned images were anonymized before being exported in DICOM format. Therefore, the only variables accessible to the investigators were the sex and the age of the patients. All images were captured between 2019 and 2020. All the samples are saved as 3D NumPy arrays with a minimum shape of [148; 265; 312] and a maximum shape of [178; 423; 463]. Hounsfield scale has been used to measure the radiodensity of CBCT volumes, and it ranges from \(-1000\) to \(4191\). The IAC occupies only a small fraction of the whole volume, which is on average \(0.1\%\). Inclusion criteria were the following:  
* Both male and female patients older than 12 years old;  
Figure 2: Example of the functioning and development of a convolutional neural network. * Patients whose identification of the IAC on CBCT images was performed either by a radiology technician or by a radiologist. Exclusion criteria were the following:
* Unclear or unreadable CBCT images;
* Presence of gross anatomical mandible anomalies, including those related to previous oncological or respective surgery. The images were then annotated to identify the course of the IAC. Sparse annotations routinely performed by the radiology technician in daily clinical practice are present in all CBCTs. The steps of the sparse annotations performed were the following:  
1. Selection of the axial slice on which to base the subsequent extraction of the simil-OPG coronal slice (Figure 3a);
2. Drawing of the curve describing the course (base curve) of the canal in the context of the body of the mandible. This curve is a spline automatically identified using the algorithm described in our previous paper and manually adjusted by adding/moving control points when needed [52] (Figure 3b);
3. Generation of the coronal slice, similar to the OPG. This is the curved plane perpendicular to the axial plane and containing the base curve (Figure 3c);
4. Bidimensional annotation of the IAC course on the coronal plane (Figure 3d). Different from above, a team of three maxillofacial surgeons devoted themselves to densely annotate CBCT volumes using our previously described tool [52]. Hence, a further dataset of CBCT volumes with both sparse and dense 3D annotations was obtained. The steps to realize it were as follows:  
1. Selection of the axial slice, on which to base the subsequent extraction of the simil-OPG coronal slice (Figure 3a);
2. Generation of the coronal slice, similar to the OPG. This is the curved plane perpendicular to the axial plane and containing the base curve (Figure 3c);
3. Bidimensional annotation of the IAC course on the coronal plane (Figure 3d);
4. Automatic generation of Cross-Sectional Lines (CSLs), i.e., lines perpendicular to the base curve and always lying on the axial plane (Figure 3e);  
Figure 3: (**a–g**): Graphical description of the steps required to create the sparse and dense annotations of the CBCT volumes. Generation and annotation of the Cross-Sectional Views (CSVs), obtained on the basis of the CLSs already described (Figure 3f). These views are planes containing the CSLs and perpendicular to the direction of the canal, which is derived from the bidimensional annotation previously obtained (Figure 3e);
6. Generation of 3D volumes (Figure 3g). The reader has probably noticed that steps from 1 to 4 of the two aforementioned annotation procedures overlap; nonetheless, the accurate annotations of CSVs in the latter make the difference to achieve a densely annotated dataset. Each step was double-checked by all members of the medical team. If the canal was not unequivocally visible in the CVSs, it was not marked in order to avoid erroneous annotations that could mislead the CNN.