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Background. Distinct, irregular, and hard nodular protuberances similar to the morphologic features of exostoses can occasionally be noted on the labial surface of the alveolar bone after orthodontic retraction of anterior teeth in adults. These have long been believed to be exostoses developed in response to loading. However, specific characterization of this phenomenon has not been documented. Case Descriptions. Three cases of patients displaying multiple irregular labial bony protuberances after retraction of anterior teeth are reported. These protuberances appeared during retraction and became more prominent with additional retraction. Serial clinical photographs, lateral cephalograms, digital models, and cone-beam computed tomography scans were evaluated. On the basis of 3-dimensional superimpositions of digital models and cone-beam computed tomographic scans, the irregular protuberances appear to be the result of differential alveolar bone modeling, with more resorption of bone covering the tooth root than that of interdental bone, and not of true bone overgrowth or deposition (that is, exostoses). Conclusion and Practical Implications. Orthodontic patients often seek treatment to improve occlusion as well as esthetics. Although this study shows that these protuberances are the result of differential modeling, they may still be perceived by patients as "outgrowths," which may cause concerns related to esthetics or comfort. Clinicians should note that these protuberances are a possible outcome when large amounts of bodily retraction and root movement of anterior teeth are planned. Patients who experience psychosocial problems with this phenomenon may be candidates for alveoloplasty. [ABSTRACT FROM AUTHOR]

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 tooth’s 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 ${0.964}\pm {0.054}$ , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of ${0}.{597}\pm {0}.{761} \, mm$ 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. [ABSTRACT FROM AUTHOR]