Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.

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.

Differential alveolar bone modeling after orthodontic retraction.

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.