Objective: To test the hypothesis that there is no difference in the stability and resistance to rotational moments of early loaded sandblasted and acid-etched (SLA) mini-implants and those of machined-surface implants of the same size and shape. Materials and Methods: A randomized complete block design was used in 12 skeletally mature male beagle dogs. Ninety-six orthodontic mini-implants were tested. Two types of implants were used: some had SLA surface treatment and some had machined surfaces without coating. After 3 weeks of healing, rotational moments of 150 g were applied. The success rates, maximum torque values, angular momentum, and total energy absorbed by the bone were compared. All values were subjected to mixed-model analysis to evaluate the influence of surface treatment, rotational force direction, and site of implantation. Results: The maximum insertion torque and angular momentum of SLA implants were significantly lower than those of machined implants (P = .034, P = .039). The SLA implants had a significantly higher value for total removal energy than the machined implants (P = .046). However, there were no significant differences in total insertion energy, maximum removal torque, and removal angular momentum between the 2 groups. There was no significant difference between clockwise and counterclockwise rotation in all measurements. Conclusion: SLA mini-implants showed relatively lower insertion torque value and angular momentum and higher total energy during removal than the machined implants, suggesting osseointegration of the SLA mini-implant after insertion.

Determining the severity of dental crowding and the necessity of tooth extraction for orthodontic treatment planning are time-consuming processes and there are no firm criteria. Thus, automated assistance would be useful to clinicians. This study aimed to construct and evaluate artificial intelligence (AI) systems to assist with such treatment planning. A total of 3,136 orthodontic occlusal photographs with annotations by two orthodontists were obtained. Four convolutional neural network (CNN) models, namely ResNet50, ResNet101, VGG16, and VGG19, were adopted for the AI process. Using the intraoral photographs as input, the crowding group and the necessity of tooth extraction were obtained. Arch length discrepancy analysis with AI-detected landmarks was used for crowding categorization. Various statistical and visual analyses were conducted to evaluate the performance. The maxillary and mandibular VGG19 models showed minimum mean errors of 0.84 mm and 1.06 mm for teeth landmark detection, respectively. Analysis of Cohen’s weighted kappa coefficient indicated that crowding categorization performance was best in VGG19 (0.73), decreasing in the order of VGG16, ResNet101, and ResNet50. For tooth extraction, the maxillary VGG19 model showed the highest accuracy (0.922) and AUC (0.961). By utilizing deep learning with orthodontic photographs, dental crowding categorization and diagnosis of orthodontic extraction were successfully determined. This suggests that AI can assist clinicians in the diagnosis and decision making of treatment plans.

Background: Taking facial and intraoral clinical photos is one of the essential parts of orthodontic diagnosis and treatment planning. Among the diagnostic procedures, classification of the shuffled clinical photos with their orientations will be the initial step while it was not easy for a machine to classify photos with a variety of facial and dental situations. This article presents a convolutional neural networks (CNNs) deep learning technique to classify orthodontic clinical photos according to their orientations. Methods: To build an automated classification system, CNNs models of facial and intraoral categories were constructed, and the clinical photos that are routinely taken for orthodontic diagnosis were used to train the models with data augmentation. Prediction procedures were evaluated with separate photos whose purpose was only for prediction. Results: Overall, a 98.0% valid prediction rate resulted for both facial and intraoral photo classification. The highest prediction rate was 100% for facial lateral profile, intraoral upper, and lower photos. Conclusion: An artificial intelligence system that utilizes deep learning with proper training models can successfully classify orthodontic facial and intraoral photos automatically. This technique can be used for the first step of a fully automated orthodontic diagnostic system in the future.

The aim of this study was to evaluate the effects of the tongue and lip pressure on dentofacial morphology. The subjects comprised 194 patients with malocclusion. Anterior and posterior tongue elevation and lip pressures were evaluated using the Iowa Oral Performance Instrument (IOPI) device. The lateral cephalograms of each subject were traced and digitized to perform the analysis. Statistical analysis was used to investigate the relationship between perioral muscle force and the cephalometric variables. Anterior and posterior tongue pressure was both higher in males than in females. No sex difference in lip pressure was observed. The group with a low posterior tongue pressure showed a short ramus height, short posterior facial height, and clockwise-rotated mandible. On the other hand, lip pressure had a significant influence on maxillary incisor angulation. Skeletal pattern was not found to be significantly related with lip pressure. The anterior tongue pressure appeared as a mixed pattern of the two results. Tongue pressure was related to skeletal measurements, such as short posterior facial height, and lip pressure was related to the angulation of the anterior teeth. This study suggests that there may be differences in dentofacial morphology according to the differences in perioral muscle force.

The purpose of this study was to compare the early or late expression levels of p65, Bcl-2, and type II myosin and the frequency of TUNEL-positive nuclei in the rat masseter muscle after injection of different concentrations of botulinum toxin-A (BTX-A). We injected either 5 U or 10 U of BTX-A into both masseter muscles of the rat. As a control group, the same volume of saline was injected. After 2 or 12 weeks, the animals were sacrificed. Subsequently, a biopsy and immunohistochemical staining of the samples were performed using a p65, Bcl-2, or type II myosin antibody. Additionally, a TUNEL assay and transmission electron microscopic analysis were performed. The expression of p65, Bcl-2, and type II myosin increased significantly with increasing concentrations of BTX-A at 2 weeks after BTX-A injection (P < 0.05). The number of TUNEL-positive nuclei was also significantly increased in the BTX-A-treated groups in comparison to the saline-treated control at 2 weeks after BTX-A injection (P < 0.05). Elevated expression of Bcl-2 was also observed in 10-unit BTX-A-treated group at 12 weeks after injection (P < 0.05). At 12 weeks after injection, the number of enlarged mitochondria was increased, and many mitochondria displayed aberrations in cristae morphology after BTX-A injection. In conclusion, BTX-A injection into the masseter muscle increased the expression level of p65, Bcl-2, and type II myosin at an early stage. The morphological changes of mitochondria were more evident at 12 weeks after injection.