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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 compare the normal eruption pattern and angulation in impacted maxillary canines using panoramic radiographs to predict maxillary canine impaction. Patients aged 6 to 15 years were classified into the normal eruption group (n = 229) and the impaction group (n = 191). At least two panoramic radiographs were taken in the normal eruption group during the eruption process of the maxillary canine. The growth pattern of the maxillary canine was analyzed using an XY coordinate system, with the tip of the maxillary lateral incisor as the origin and the tooth's long axis as the Y-axis and measurement of the relative position of the crown tip and angulation of the maxillary canine. The crown tips of normally erupted maxillary canines were intensively distributed along the distal surface of the maxillary lateral incisor, while those of impacted canines were widely distributed. The angulations of the normally erupted canines increased as eruption increased along the lateral incisor and then decreased at the cervical point of the lateral incisor. The angulations of the impacted canines were scattered, with no uniform pattern. While using the normal eruption path of the maxillary canine and the pattern of change in angulation based on the distal surface of the maxillary lateral incisor, early intervention or regular follow-up is needed to prevent maxillary canine impaction. [ABSTRACT FROM AUTHOR]

Objective: To examine the robustness of the published machine learning models in the prediction of extraction vs non‐extraction for a diverse US sample population seen by multiple providers. Setting and Sample Population: Diverse group of 838 patients (208 extraction, 630 non‐extraction) were consecutively enrolled. Materials and Methods: Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training. Results: Performance of the models (75%‐79% ACC and 72%‐76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1‐NB) and G2 (age, mandibular crowding and lower lip to E‐plane). Conclusions: An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision‐making. [ABSTRACT FROM AUTHOR]

Orthodontic forces are strongly associated with pain, the primary complaint among patients wearing orthodontic braces. Compared to other side effects of orthodontic treatment, orthodontic pain is often overlooked, with limited clinical management. Orthodontic forces lead to inflammatory responses in the periodontium, which triggers bone remodeling and eventually induces tooth movement. Mechanical forces and subsequent inflammation in the periodontium activate and sensitize periodontal nociceptors and produce orthodontic pain. Nociceptive afferents expressing transient receptor potential vanilloid subtype 1 (TRPV1) play central roles in transducing nociceptive signals, leading to transcriptional changes in the trigeminal ganglia. Nociceptive molecules, such as TRPV1, transient receptor potential ankyrin subtype 1, acid-sensing ion channel 3, and the P2X3 receptor, are believed to mediate orthodontic pain. Neuropeptides such as calcitonin gene-related peptides and substance P can also regulate orthodontic pain. While periodontal nociceptors transmit nociceptive signals to the brain, they are also known to modulate alveolar bone remodeling in periodontitis. Therefore, periodontal nociceptors and nociceptive molecules may contribute to the modulation of orthodontic tooth movement, which currently remains undetermined. Future studies are needed to better understand the fundamental mechanisms underlying neuroskeletal interactions in orthodontics to improve orthodontic treatment by developing novel methods to reduce pain and accelerate orthodontic tooth movement-thereby achieving "big gains with no pain" in clinical orthodontics., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (© 2024 Wang, Ko and Chung.)

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.)