The purpose of this study was to integrate the available data published to date on susuk or charm needles into a comprehensive analysis of their clinical/radiological features. An electronic search was undertaken in September 2019. Eligibility criteria included publications having enough clinical and radiological to confirm a definite diagnosis. The initial literature search resulted in 48 publications. Ten publications were excluded for duplicates, and another 17 excluded after a screening of the abstract. Besides, the screening of the abstract shows that five publications were not meeting the inclusion criteria, resulting in a total of 14 publications of susuk that were included in the systematic review. Bias analysis was conducted according to Oxford Center for Evidence-Based Medicine. The resulting total of 78 cases from the selected publications were analysed, showing a wide age range with different distribution among gender and ethnicity. Three cases reported in the literature having symptoms related to susuk. Susuk can be seen as an incidental finding during a routine radiographic assessment, and clinicians should be able to differentiate it from other radiopaque foreign bodies. The practice is not limited to South East Asian population and can be seen in wide racial profiles.

Purpose: The objective of this scoping review was to investigate the applicability and performance of various convolutional neural network (CNN) models in tooth numbering on panoramic radiographs, achieved throughclassification, detection, and segmentation tasks. Materials and Methods: An online search was performed of the PubMed, Science Direct, and Scopus databases.Based on the selection process, 12 studies were included in this review. Results: Eleven studies utilized a CNN model for detection tasks, 5 for classification tasks, and 3 for segmentationtasks in the context of tooth numbering on panoramic radiographs. Most of these studies revealed high performance of various CNN models in automating tooth numbering. However, several studies also highlighted limitations of CNNs, such as the presence of false positives and false negatives in identifying decayed teeth, teeth with crown prosthetics, teeth adjacent to edentulous areas, dental implants, root remnants, wisdom teeth, and root canal-treated teeth. These limitations can be overcome by ensuring both the quality and quantity of datasets, as well as optimizing the CNN architecture. Conclusion CNNs have demonstrated high performance in automated tooth numbering on panoramic radiographs.Future development of CNN-based models for this purpose should also consider different stages of dentition, such as the primary and mixed dentition stages, as well as the presence of various tooth conditions. Ultimately, an optimized CNN architecture can serve as the foundation for an automated tooth numbering system and for further artificial intelligence research on panoramic radiographs for a variety of purposes.

Introduction: Hyaluronic acid (HA) has a long history and is widely used in cosmetics, medicine, and dermatology. This molecule is still considered relatively new in the field of dentistry. This study aimed to assess the application of HA in dental implant treatment. Method: Search in the multiple indexed databases such as Pubmed, COCHRANE, and Scopus was conducted up until August 2022 using the keywords “hyaluronic acid”, “hyaluronan,” and “dental implant.” Results: The literature search identified 816 articles, and 17 were selected in this study. Three domains of use of HA in dental implant treatment were identified: surface modification of implant surface, treatment after insertion of a dental implant, and bone graft/membrane material. There are eight randomized control trials and nine non-randomized control trials included in this study. Only six studies showed statistically significant results with HA groups. Conclusion: Overall, there are positive findings on the application of HA in dental implant treatment, showing it can be used in dental implantology, with multiple categories of uses.