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Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists experiences due to the abnormality and large-scale variance of patients teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooths region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.964±0.054 , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of 0.597±0.761 mm in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.

To identify predictors regarding the type and severity of malocclusion that affect total Invisalign treatment duration based on an intraoral digital scan. The subjects of this retrospective clinical cohort were 116 patients treated with Invisalign. A deep learning method was used for automated tooth segmentation and landmark identification of the initial and final digital models. The changes in the six degrees of freedom (DOF), representing types of malalignment, were measured. Linear regression was performed to find the contributing factors associated with treatment time. In addition, the Peer Assessment Rating (PAR) score and a composite score combining 6 DOF were correlated separately to the treatment time. The number of trays differed between sexes (P =.0015). The absolute maximum torque was marginally associated with the total number of trays (P =.0518), while the rest of the orthodontic tooth movement showed no correlation. The composite score showed a higher correlation with the total number of trays (P =.0045) than did individual tooth movement. Pretreatment upper and lower anterior segment PAR scores were positively associated with the treatment time (P <.001). There is not enough evidence to conclude that certain types of tooth movement affect the total aligner treatment time. A composite score seems to be a better predictor for total treatment time than do individual malalignment factors in aligner treatment. Upper and lower anterior malalignment factors have a significant effect on the treatment duration. [ABSTRACT FROM AUTHOR]

To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information. A machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) was used with cone-beam computed tomography (CBCT) images to quantify volumetric skeletal maxilla discrepancies of 30 study group (SG) patients with unilaterally impacted maxillary canines and 30 healthy control group (CG) subjects. Fully automatic segmentation was implemented for maxilla isolation, and maxillary volumetric and linear measurements were performed. Analysis of variance was used for statistical evaluation. Maxillary structure was successfully auto-segmented, with an average dice ratio of 0.80 for three-dimensional image segmentations and a minimal mean difference of two voxels on the midsagittal plane for digitized landmarks between the manually identified and the machine learning–based (LINKS) methods. No significant difference in bone volume was found between impaction ([2.37 ± 0.34] 104 mm3) and nonimpaction ([2.36 ± 0.35] 104 mm3) sides of SG. The SG maxillae had significantly smaller volumes, widths, heights, and depths (P <.05) than CG. The data suggest that palatal expansion could be beneficial for those with unilateral canine impaction, as underdevelopment of the maxilla often accompanies that condition in the early teen years. Fast and efficient CBCT image segmentation will allow large clinical data sets to be analyzed effectively. [ABSTRACT FROM AUTHOR]

Objectives: To identify predictors regarding the type and severity of malocclusion that affect total Invisalign treatment duration based on an intraoral digital scan., Materials and Methods: The subjects of this retrospective clinical cohort were 116 patients treated with Invisalign. A deep learning method was used for automated tooth segmentation and landmark identification of the initial and final digital models. The changes in the six degrees of freedom (DOF), representing types of malalignment, were measured. Linear regression was performed to find the contributing factors associated with treatment time. In addition, the Peer Assessment Rating (PAR) score and a composite score combining 6 DOF were correlated separately to the treatment time., Results: The number of trays differed between sexes (P = .0015). The absolute maximum torque was marginally associated with the total number of trays (P = .0518), while the rest of the orthodontic tooth movement showed no correlation. The composite score showed a higher correlation with the total number of trays (P = .0045) than did individual tooth movement. Pretreatment upper and lower anterior segment PAR scores were positively associated with the treatment time (P < .001)., Conclusions: There is not enough evidence to conclude that certain types of tooth movement affect the total aligner treatment time. A composite score seems to be a better predictor for total treatment time than do individual malalignment factors in aligner treatment. Upper and lower anterior malalignment factors have a significant effect on the treatment duration., (© 0000 by The EH Angle Education and Research Foundation, Inc.)

Background/aim: Although anticancer effects of sorafenib on renal, liver and colon carcinomas are well-known, its combination effect with ionizing radiation on oral squamous cell carcinoma (OSCC) is unclear. Herein human SAS cells, an OSCC cell line, were used in order to elucidate this combination effect., Materials and Methods: Both SAS and SAS/nuclear factor kappa-B-luciferase (SAS/NF-κB-luc2) cell lines were used in the study. Cell viability, NF-κB activation, and protein expression of NF-κB downstream effectors were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, NF-κB-luc2 reporter gene system, NF-κB/DNA binding activity and western blotting., Results: Sorafenib significantly increased radiation-induced cytotoxicity and apoptosis via both mitochondrial-dependent and independent pathways. In addition, NF-κB activity and downstream effector protein expression induced by radiation was suppressed by sorafenib in SAS/NF-κB-luc2 cells., Conclusion: Combination of sorafenib with radiation for the treatment of human OSCC shows a synergistic effect via suppression of radiation-induced NF-κB activity and its regulated downstream effector proteins., (Copyright © 2014 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.)

Background: It has been shown that cytochrome P450 enzymes (CYPs) and acetyltransferase can be used as biomarkers of carcinogen-DNA adduct levels and human cancer susceptibility. The gastrointestinal tract is the portal of entry of foreign compounds and presents xenobiotic metabolizing N-acetyltransferase (NAT) and CYPs activities. 5-Methoxypsoralen (5-MOP) has been used in combination with UV radiation in skin photochemotherapy for decades. A number of studies have demonstrated that 5-MOP is inhibitory towards mouse and human CYP isoforms, but investigations on the direct effects on NAT activity in laboratory animals and human cancer cells are limited. The main objective of this study was to document the effects of 5-MOP on the modulation of NAT activities in the stomach and colon of rats and human stomach and colon tumor cell lines., Materials and Methods: N-Acetylation of 2-aminofluorene (AF) to 2-acetylaminofluorene (AAF) by NAT in the stomach and colon of Sprague-Dawley (SD) rats and in human stomach (SC-M1) and colon (COLO 205) tumor cell lines was investigated., Results: The data show that the metabolic activity of NAT in the rat colon was higher than that in the rat stomach, and the further metabolism of AAF was slower in the stomach than in the colon. 5-MOP increased the activity of NATand also increased the further metabolism of AAF at 24 h in the rat stomach. In the rat colon, no statistically significant changes caused by 5-MOP were observed in NAT activity, but 5-MOP increased the further metabolism of AAF at 24 to 72 h. 5-MOP decreased the activity of NAT only at 72-h incubation in SC-M1 cells. In COLO 205 cells, however, 5-MOP decreased the activity of NAT between 24 h and 72 h. The optimal concentrations of 5-MOP to induce decreased NAT activity in SC-M1 cells were 0.05 mM to 25 mM. In COLO 205 cells, the data indicate that the higher the concentrations of 5-MOP, the higher the acetylation of AF; a promotion effect of NAT activity occured at a higher dose (50 mM) of 5-MOP and an inhibition effect occured at lower doses (0.05-0.5 mM) of 5-MOP, while concentrations of 5-25 mM of 5-MOP showed no significant difference compared with the control regimen., Conclusion: The metabolic activity of NAT in the rat colon was higher than that in the rat stomach, and the results also showed a high degree of correspondence with SC-M1 cells and COLO 205 cells. 5-MOP more efficiently inhibited NAT activity in human stomach and colon tumor cell lines than in the stomach and colon of rats.