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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]

Objective: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol.Setting and Sample Population: Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.52 years (SD = 3.27 years; range of 8-18 years).Materials and Methods: The deep-learning-based protocol was used to segment the maxilla and defect initially, followed by manual refinement. Paired t-tests were performed to characterize the maxillary asymmetry. A multiple linear regression was carried out to investigate the relationship between the defect parameters and those of the cleft side of the maxilla.Results: The cleft side of the maxilla demonstrated a significant decrease in maxillary volume and length as well as alveolar length, anterior width, posterior width, anterior height and posterior height. A significant increase in maxillary anterior width was demonstrated on the cleft side of the maxilla. There was a close relationship between the defect parameters and those of the cleft side of the maxilla.Conclusions: Based on the 3D volumetric segmentations, significant hypoplasia of the maxilla on the cleft side existed in the pyriform aperture and alveolar crest area near the defect. The defect structures appeared to contribute to the variability of the maxilla on the cleft side. [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.)