Gingival epithelial-like cells (GE-1) were cultured and used to examine the cellular responses of gingival tissues to varying concentrations of titanium (Ti) ions. Titanium ions at concentrations of more than 13 ppm significantly decreased the viability of GE-1 cells and increased LDH release from the cells into the supernatant, but had no significant effect on their caspase 3 activity. These data suggest that a high concentration of Ti ions induced necrosis of the GE-1 cells. Titanium ions at a concentration of 5 ppm significantly increased the level of CCL2 mRNA expression in GE-1 cells exposed to lipopolysaccharide derived from Porphyromonas gingivalis in a synergistic manner. Moreover, the mRNA expression levels of TLR-4 and ICAM-1 in GE-1 cells loaded with Ti ions at 9 ppm were significantly enhanced as compared with those in GE-1 cells without Ti stimulation. We suggest that Ti ions are in part responsible for monocyte infiltration in the oral cavity by elevating the sensitivity of gingival epithelial cells to microorganisms. Taken together, these data indicate that Ti ions may be involved in cytotoxicity and inflammation at the interfaces of dental implants and gingival tissue.

Background: Supernumerary teeth are a common anomaly and are frequently observed in paediatric patients. To prevent or minimize complications, early diagnosis and treatment is ideal in children with supernumerary teeth. Aim: This study aimed to apply convolutional neural network (CNN)-based deep learning to detect the presence of supernumerary teeth in children during the early mixed dentition stage. Design: Three CNN models, AlexNet, VGG16-TL, and InceptionV3-TL, were employed in this study. A total of 220 panoramic radiographs (from children aged 6 years 0 months to 9 years 6 months) including supernumerary teeth (cases, n = 120) or no anomalies (controls, n = 100) were retrospectively analyzed. The CNN performances were assessed according to accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and area under the ROC curves for a cross-validation test dataset. Results:The VGG16-TL model had the highest performance according to accuracy, sensitivity, specificity, and area under the ROC curve, but the other models had similar performance. Conclusion:CNN-based deep learning is a promising approach for detecting the presence of supernumerary teeth during the early mixed dentition stage.

The aim of the feasibility study was to construct deep learning models for the classification of multiple dental anomalies in panoramic radiographs. Panoramic radiographs with single supernumerary teeth and/or odontomas were considered the "case" group; panoramic radiographs with no dental anomalies were considered the "control" group. The dataset comprised 150 panoramic radiographs: 50 each of no dental anomalies, single supernumerary teeth, and odontomas. To classify the panoramic radiographs into case and control categories, we employed AlexNet, which is a convolutional neural network model. AlexNet was able to classify whole panoramic radiographs into two or three classes, according to the presence or absence of supernumerary teeth or odontomas. The performance metrics of the three-class classification were 70%, 70.8%, 70%, and 69.7% for accuracy, precision, sensitivity, and F1 score, respectively, in the macro average. These results support the feasibility of using deep learning to detect multiple dental anomalies in panoramic radiographs.

Purpose:The purpose of this study was to perform an in vitro evaluation of digital impressions using a mobile device and monoscopic photogrammetry in cases of orbital defects with undercuts. Methods: Three 10-mm-square cubes were attached to a diagnostic cast of a patient with a right orbital defect. Still images acquired with a mobile device were used to generate facial three-dimensional (3D) data. Two types of still images were used: one was a whole face image, and the other was a defect site-focused image. For comparison, an extraoral scanner was used to obtain facial 3D data. Five dental technicians fabricated 3D printed models using additive manufacturing and measured the distances between the measurement points using a digital caliper. The discrepancy between the distances measured on the diagnostic cast of the patient and the 3D printed model was calculated. Friedman test was used to analyze the discrepancy, and the Bonferroni test was used to verify the differences between the pairs. Results: Statistical significance was found with respect to the type of 3D model fabrication method. Conclusion: Within the limitations of this in vitro study, the results suggested that the workflow can be applied to digital impressions of the maxillofacial region.