## 5 Discussion
To the best of our knowledge, our result of 0.79 in terms of Dice score was the best ever recorded, setting a new state of the art in the field of the segmentation of the IAC. Once again, it is also interesting to notice that the number of CBCT volumes we used (91 volumes) was much lower than [PERSON]'s one (235 volumes). Together with the better results achieved, this demonstratesthe successfulness of the optimized algorithm, which could make the dataset much more informative even with a lower number of CBCTs. It Is also interesting to observe the improvement of the results in the secondary end-point, i.e., the time required for the development of the annotated volumes. Cost-effectiveness in medicine, and especially in radiology, is becoming a critical topic. In recent years, there has been an increasing interest in extending the amount of radiologic data produced, while reducing the quantity of hours dedicated to their extraction. Therefore, we focused on time as a secondary end-point, in order to measure the effectiveness of our algorithm also in terms of processing speed. As previously stated, there has been a critical reduction in the amount of time required to segmentate the IAC: the CNN is able to accurately annotate it in less than 7.5% of the time needed by the radiology technician (6.33 s vs. 87.3 s). In addition, only the data produced by the present CNN are voxel-based, while the data composed by the radiology technician in the clinical practice are bidimensional. So, the main advantages reached in these investigations are two: waste of time reduction and improvement in the quality of the information obtained. In fact, a few clicks are enough to obtain a reliable annotation of the mandibular canal using our CNN. During the daily work, it takes more than a minute for the radiology technicians to identify that structure. The time saved can allow the operator to devote his/her time to other tasks while the software performs the annotations independently. Moreover, the annotations conventionally obtained are bidimensional. On the contrary, a properly trained CNN can provide tridimensional identification of the mandibular canal, thus allowing for example a better pre-operative planning of a surgical intervention. The integration of our CNN in a multidisciplinary clinical practice can enable better cost-effectiveness. The beneficiaries will be many:  
* The surgeon has access to a three-dimensional annotation of the IAC, thus being able to better visualize the data and better plan the surgical procedures (i.e., positioning of a dental implant or approaching to an impacted tooth). * The radiologist can examine the CBCT volume and describe the IAC course more detailly. * The standard of care would be improved, providing the patient a safer and more predictable morphological diagnosis. * The waste of time for the radiology technician is minimized, while maximizing the amount of information provided to the clinicians. * The radiology center would increase the cost-effectiveness of the CBCT exam. A final consideration is to be made regarding the possibility to access the full anonymized dataset, which can be consulted at the following address: at [[https://ditto.ing.unimore.it/maxillo/](https://ditto.ing.unimore.it/maxillo/)]([https://ditto.ing.unimore.it/maxillo/](https://ditto.ing.unimore.it/maxillo/)) (accessed on 28 February 2023). Along with it, the source code was also made accessible to all. To the best of our knowledge, this is the first time in the medical scientific literature. Yet, we firmly believe in a common effort towards science development, and we hope this choice will encourage more researchers to do the same. The main limitation of this paper is the low number of densely annotated CBCT volumes. This aspect will soon be improved by enhancing the number of voxel-based annotations of the mandibular canal in new CBCT volumes.