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.

In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, resulting in a lack of comparative evaluation studies on a common benchmark. To address this scientific gap and encourage deep learning research in the field, the ToothFairy challenge was organized within the MICCAI 2023 conference. In this context, a public dataset was released to also serve as a benchmark for future research. The dataset comprises 443 CBCT scans, with voxel-level annotations of the IAC available for 153 of them, making it the largest publicly available dataset of its kind. The participants of the challenge were tasked with developing an algorithm to accurately identify the IAC using the 2D and 3D-annotated scans. This paper presents the details of the challenge and the contributions made by the most promising methods proposed by the participants. It represents the first comprehensive comparative evaluation of IAC segmentation methods on a common benchmark dataset, providing insights into the current state-of-the-art algorithms and outlining future research directions. Furthermore, to ensure reproducibility and promote future developments, an open-source repository that collects the implementations of the best submissions was released.

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.

Background: Port-wine stains (PWS) are congenital low-flow vascular malformations of the skin. PWS tend to become thicker and darker with time. Laser therapy is the gold standard and the first-line therapy for treating PWS. However, some resistant PWS, or PWS that have tissue hypertrophy, do not respond to this therapy. Our aim is to evaluate the role of surgery in the treatment of PWS birthmarks. Methods: A literature search was performed in PubMed, Scopus, Web of Science (WOS) and Google Scholar for all papers dealing with surgery for port-wine stains, from January 2010 to December 2020 using the search strings: (capillary vascular malformation OR port-wine stains OR Sturge Weber Syndrome OR sws OR pws) AND (surgical OR surgery). Results: Ten articles were identified and used for analysis. They were almost all case series with a short follow up period and lacked an objective–systematic score of evaluation. Conclusions: Delay in treatment of port wine stains may result in soft tissue and bone hypertrophy or nodules with disfiguring or destructive characteristics. The correction of PWS-related facial asymmetry often requires bone surgery followed by soft tissue corrections to achieve a more harmonious, predictable result.