Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article.

Osteotomy is a common step in oncological, reconstructive, and trauma surgery. Drilling and elevated temperature during osteotomy produce thermal osteonecrosis. Heat and associated mechanical damage during osteotomy can impair bone healing, with consequent failure of fracture fixation or dental implants. Several ex vivo studies on animal bone were recently focused on heating production during osteotomy with conventional drill and piezoelectric devices, particularly in endosseous dental implant sites. The current literature on bone drilling and osteotomic surface analysis is here reviewed and the dynamics of bone healing after osteotomy with traditional and piezoelectric devices are discussed. Moreover, the methodologies involved in the experimental osteotomy and clinical studies are compared, focusing on ex vivo and in vivo findings.

Biophysical energies are a versatile tool to stimulate tissues by generating biopotentials. In particular, pulsed electromagnetic field (PEMF) stimulation has intrigued researchers since the 1970s. To date, many investigations have been carried out in vivo, but a gold standard treatment protocol has not yet been defined. The main obstacles are represented by the complex setting of PEMF characteristics, the variety of animal models (including direct and indirect bone damage) and the lack of a complete understanding of the molecular pathways involved. In the present review the main studies about PEMF stimulation in animal models with bone impairment were reviewed. PEMF signal characteristics were investigated, as well as their effect on molecular pathways and osseous morphological features. We believe that this review might be a useful starting point for a prospective study in a clinical setting. Consistent evidence from the literature suggests a potential beneficial role of PEMF in clinical practice. Nevertheless, the wide variability of selected parameters (frequency, duration, and amplitude) and the heterogeneity of applied protocols make it difficult to draw certain conclusions about PEMF effectiveness in clinical implementation to promote bone healing. Deepening the knowledge regarding the most consistent results reported in literature to date, we believe that this review may be a useful starting point to propose standardized experimental guidelines. This might provide a solid base for further controlled trials, to investigate PEMF efficacy in bone damage conditions during routine clinical practice.

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.

The effect of multiple general anesthesia (mGA) procedures administered in early life is a critical theme and has led the Food and Drug Administration (FDA) to issue an alert. This systematic review seeks to explore the potential effects on neurodevelopment of mGA on patients under 4 years. The Medline, Embase and Web of Science databases were searched for publications up to 31 March 2021. The databases were searched for publications regarding “children multiple general anesthesia OR pediatric multiple general anesthesia”. Case reports, animal studies and expert opinions were excluded. Systematic reviews were not included, but they were screened to identify any possible additional information. A total of 3156 studies were identified. After removing the duplicates, screening the remaining records and analyzing the systematic reviews’ bibliography, 10 studies were considered suitable for inclusion. Comprehensively, a total cohort of 264.759 unexposed children and 11.027 exposed children were assessed for neurodevelopmental outcomes. Only one paper did not find any statistically significant difference between exposed and unexposed children in terms of neurodevelopmental alterations. Controlled studies on mGA administered before 4 years of age support that there might be a greater risk of neurodevelopmental delay in children receiving mGA, warranting the need for careful risk/benefit considerations.

Objectives: The aim of this retrospective study was to determine the incidence and the clinical outcome of tongue cancer (TC) in patients affected by Fanconi anemia (FA) who received an allogeneic hematopoietic cell transplantation (HCT). Materials and methods: The patient database from the Bone Marrow Transplant Center of Pescara was reviewed to enroll FA patients. Patients’, donors’, HCT’s, and screening’s data were collected as well to look for the incidence and the treatment of TC. Results: Twelve patients affected by FA were identified. Three patients died for transplant-related causes. Five of nine surviving patients were diagnosed with TC at a median of 21.7 years since transplantation and at a median age of 32.10 years. Interestingly, no patient manifested graft-versus-host-disease (GvHD). The 28-year cumulative incidence function of TC was 46.9% (95% CI, 36.9–56.9%). Two patients were treated with chemotherapy alone, two patients were treated with surgery alone, and one with surgery followed by chemotherapy. Overall, 4 patients with TC showed a clinical course characterized by a marked aggressiveness of the tumor disease which led to death due to cancer progression between 2 and 13 months. One patient is surviving 8 months after diagnosis of TC. Conclusions: Our study confirms the high incidence of tumors and in particular tongue tumors in allotransplanted FA patients. A careful screening has to be life-long maintained. Clinical relevance: Considering the rarity of FA and the frailty of FA patients, this study may add important information for the cancer management of these patients.