The Coronavirus disease 2019 (COVID-19) pandemic suddenly took the world by storm and Italy was one of the hardest hit countries. Maxillo-facial surgery and dentistry procedures had to be significantly reorganized, since they are considered high-risk procedures. Protocols had to be changed and interdepartmental cooperation was put in place to plan surgical interventions and maintain high standards. Various improvements have been made to prevent and reduce the risks of spreading the infection. Even if the situation seems to have improved, being unprepared is not an option. In this paper the experience gained during these months has been shared and possible future challenges has been highlighted, suggesting practical adjustments based also on new guidelines and recommendations.

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Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation.

Reconstruction of defects of the jaws is mainly performed via free fibula flap. An incidence of 2–21% of overall flap failure is still described. We investigated the roles of volume, length and number of fibula flap segments on flap survival using novel three-dimensional segmentation tools. We also analyzed the role of other possible risk factors. Seventy-one consecutive patients with a follow up of at least three months and who underwent free fibula flap reconstruction in a single center between 2002 and 2022 have been evaluated. A total of 166 fibula segments were analyzed. Malignancies were the main reason of resection (45.1%). In 69% of the cases a reconstruction of the mandible was performed. The flaps were mainly divided in two segments (39%) (range 1–4), with a mean length of 2.52 cm and a mean volume was 3.37 cm3. Total flap failure (TFF) occurred in 12 cases, (16.9%), while partial flap failure (PFF) appeared in 3 patients (4.2%). Volume, length and number of fibula flap segments did not seem to influence flap failure incidence in uni- and multivariate analysis. Reconstruction of the maxilla and use of a recipient vessel different from the facial artery seemed to significantly impact on flap failure. Smoking and previous surgeries showed a higher trend to flap failure, but they did not reach statistical significance. Prospective and multicentric analysis on a wider population should be assessed.

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