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.

Background: Gender identification by using skeletal identification is an important tool in forensic, medico-legal, bioarkeology, and anthropology. Traditional morphological methods depended on the anthropologist subjectivity that caused a significant difference among the observer. This study aims to develop the discriminant function for gender prediction in a Surabaya-Indonesia population age ranges 15-25-year-olds by using a panoramic radiograph. This research used 273 panoramic radiographs consisted of 129 male panoramic radiographs and 144 female panoramic radiographs. The researchers measured 11 parameters of the jaw such as two gonial angles, two mandibular ramus heights, two mandibular ramus widths, two mandibular corpus lengths, two nasal line maxilla, and anterior mandibular corpus heights. The researchers analyzed the data by using the discriminant analysis of the IBM SPSS statistic 24. Results: The result of our study shows there were significant differences in the jaw morphometry between males and females, except the mandibular ramus widths. The jaw size in males was larger than that of the female. The biggest dimorphism variables based on the Wilks lambda value were gonial angles, mandibular ramus heights, mandibular corpus lengths, and nasal lines. While the smallest dimorphism variables were mandibular ramus widths. The accuracy of discriminant analysis for each variable ranges from 47.3% to 93.8%. Conclusion This preliminary study in Surabaya-Indonesia population age ranges 15-25-year-olds by using panoramic radiograph shows the highest accuracy of gender prediction by using discriminant function was obtained from the combination of the nine jaw parameters.
Gender Identity ; Maxilla