Objectives: To explore risk factors for contact between the incisive canal (IC) and upper central incisors (U1) and to evaluate the relationship between contact and root resorption using cone-beam computer tomography (CBCT). Materials and Methods: This retrospective study used CBCT data of 33 patients treated by a senior orthodontist. Anterior teeth were retracted with mini-implants, and CBCT scans were taken before and after retraction. IC height and width, U1 lingual movement, and U1-IC distance and root length decrease were compared between contact and noncontact groups. Results: Sixteen U1 roots in 11 patients touched the IC. The contact group had lower positioned ICs (2.86 ± 1.10 mm) than the noncontact group (4.07 ± 1.72 mm). The middle of the U1 roots showed more lingual movement to ICs in the contact group (2.30 ± 1.20 mm) than in the noncontact group (1.07 ± 1.16 mm). Right central incisors were closer to the IC than were the left. Root length decreased significantly more in the contact group (2.63 ± 0.93 mm) than in the noncontact group (1.14 ± 0.83 mm). Conclusions: There is a risk for the U1 root to contact the IC during anterior retraction when the IC is lower positioned. This contact might cause external apical root resorption.

Oral squamous cell carcinoma (OSCC) is one of the top 15 most prevalent cancers worldwide. However, the current treatment models for OSCC (e.g., surgery, chemotherapy, radiotherapy, and combination therapy) present several limitations: damage to adjacent healthy tissue, possible recurrence, low efficiency, and severe side effects. In this context, nanomaterial-based photothermal therapy (PTT) has attracted extensive research attention. This paper reviews the latest progress in the application of biological nanomaterials for PTT in OSCC. We divide photothermal nanomaterials into four categories (noble metal nanomaterials, carbon-based nanomaterials, metal compounds, and organic nanomaterials) and introduce each category in detail. We also mention in detail the drug delivery systems for PTT of OSCC and briefly summarize the applications of hydrogels, liposomes, and micelles. Finally, we note the challenges faced by the clinical application of PTT nanomaterials and the possibility of further improvement, providing direction for the future research of PTT in OSCC treatment.

Background: It is difficult for orthodontists to accurately predict the growth trend of the mandible in children with anterior crossbite. This study aims to develop a deep learning model to automatically predict the mandibular growth result into normal or overdeveloped using cephalometric radiographs. Methods: A deep convolutional neural network (CNN) model was constructed based on the algorithm ResNet50 and trained on the basis of 256 cephalometric radiographs. The prediction behavior of the model was tested on 40 cephalograms and visualized by equipped with Grad-CAM. The prediction performance of the CNN model was compared with that of three junior orthodontists. Results: The deep-learning model showed a good prediction accuracy about 85%, much higher when compared with the 54.2% of the junior orthodontists. The sensitivity and specificity of the model was 0.95 and 0.75 respectively, higher than that of the junior orthodontists (0.62 and 0.47 respectively). The area under the curve value of the deep-learning model was 0.9775. Visual inspection showed that the model mainly focused on the characteristics of special regions including chin, lower edge of the mandible, incisor teeth, airway and condyle to conduct the prediction. Conclusions: The deep-learning CNN model could predict the growth trend of the mandible in anterior crossbite children with relatively high accuracy using cephalometric images. The deep learning model made the prediction decision mainly by identifying the characteristics of the regions of chin, lower edge of the mandible, incisor teeth area, airway and condyle in cephalometric images.

Background: Facial esthetics is a major concern of orthodontic patients. This study aims to evaluate orthodontic treatment-related thickness changes of the masseter muscles and surrounding soft tissues and the potential factors that would influence these changes during orthodontic treatment in female adults. Methods: Forty-two female adult patients were included in this retrospective study and were divided into extraction (n = 22) and nonextraction (n = 20) groups. Pretreatment and posttreatment cone-beam computed tomography (CBCT) images were superimposed and reconstructed. The thickness changes of the masseter area of facial soft tissue (MAS), masseter muscles (MM) and surrounding fat tissue (FT) were measured. Pretreatment age, treatment duration, sagittal relationship (ANB), and vertical relationship (Frankfort-mandibular plane angle, FMA)-related MAS, MM and FT changes were compared between extraction and nonextraction groups. Spearman’s correlation coefficient was calculated between the above variables. Regression analysis was conducted to confirm the causal relations of the variables. Results: The thickness of MAS and MM significantly decreased in both groups, with larger decreases (> 1 mm) in the extraction group. There were strong correlations (r > 0.7) between the thickness decrease in MAS and MM in both groups and moderate correlations (r > 0.4) between MAS and FT in the nonextraction group. A significantly greater decrease of MAS and MM were found to be moderately correlated with a smaller FMA (r > 0.4) in the extraction group. Scatter plots and regression analysis confirmed these correlations. Conclusions: Masseter muscles and the surrounding soft tissue exhibited a significant decrease in thickness during orthodontic treatment in female adults. Low-angle patients experienced a greater decrease in soft tissue thickness in the masseter area in the extraction case. But the thickness changes were clinically very small in most patients.

Background: Machine learning based auto-segmentation of 3D images has been developed rapidly in recent years. However, the application of this new method in the research of patients with unilateral cleft lip and palate (UCLP) is very limited. In this study, a machine learning algorithm utilizing 3D U-net was used to automatically segment the maxilla, fill the cleft and evaluate the alveolar bone graft in UCLP patients. Cleft related factors and the surgery impact on the development of maxilla were analyzed. Methods: Preoperative and postoperative computed tomography images of 32 patients (64 images) were obtained. The deep-learning-based protocol was used to segment the maxilla and defect, followed by manual refinement. Paired t-tests and Mann-Whitney tests were performed to reveal the changes of the maxilla after surgery. Two-factor, two-level analysis for repeated measurement was used to examine the different trends of growth on the cleft and non-cleft sides of the maxilla. Pearson and Spearman correlations were used to explore the relationship between the defect and the changes of the maxillary cleft side. Results: One-year after the alveolar bone grafting surgery, different growth amount was found on the cleft and non-cleft sides of maxilla. The maxillary length (from 34.64 ± 2.48 to 35.67 ± 2.45 mm) and the alveolar length (from 36.58 ± 3.21 to 37.63 ± 2.94 mm) increased significantly only on the cleft side while the maxillary anterior width (from 11.61 ± 1.61 to 12.01 ± 1.41 mm) and posterior width (from 29.63 ± 2.25 to 30.74 ± 2.63 mm) increased significantly only on the non-cleft side after surgery. Morphology of the cleft was found to be related to the pre-surgical maxillary dimension on the cleft side, while its correlation with the change of the maxilla after surgery was low or not statistically significant. Conclusion: The auto-segmentation of the maxilla and the cleft could be performed very efficiently and accurately with the machine learning method. Asymmetric growth was found on the cleft and non-cleft sides of the maxilla after alveolar bone graft in UCLP patients. The morphology of the cleft mainly contributed to the pre-operation variance of the maxilla but had little impact on the maxilla growth after surgery.