Research progress on deep learning algorithms to assist 3D tooth segmentation of digital dental models / 口腔疾病防治

@#Three-dimensional tooth segmentation is the segmentation of single-tooth models from a digital dental model. It is an important foundation for diagnosis, planning, treatment and customized appliance manufacturing in digital orthodontics. With the deep integration of artificial intelligence technology and big data from stomatology, the use of deep learning algorithms to assist 3D tooth segmentation has gradually become mainstream. This review summarizes the current situation of deep learning algorithms that assist 3D tooth segmentation from the aspects of dataset establishment, algorithm architecture, algorithm performance, innovation and advantages, deficiencies of current research and prospects. The results of the literature review showed that deep learning tooth segmentation methods could obtain an accuracy of more than 95% and had good robustness. However, the segmentation of complex dental models, operation time and richness of the training database still need to be improved. Research and development of the "consumption reduction and strong core" algorithm, establishment of an authoritative data sample base with multiple centers, and expansion of data application depth and breadth will lead to further development in this field.

Research progress on root position measurement methods in orthodontic treatment using cone beam CT / 口腔疾病防治

@#Root position plays an important role in healthy, stable, and aesthetic orthodontic treatment. In the past, two-dimensional radiographic images were used to assess the accuracy and precision of tooth root positions. In recent years, the use of cone beam CT (CBCT) and its reconstructed images to measure the three-dimensional spatial position and angle of root position has become mainstream. The root position is mainly described by measuring the relationship between the root and adjacent structures in the buccolingual, vertical, and mesiodistal directions as well as the root angle. The thickness of the alveolar bone on the buccolingual side of the root represents the buccolingual position, the vertical height in the alveolar bone and the relationship between apex and maxillary sinus represents the vertical position, the interroot alveolar bone thickness represents the mesiodistal position of the root, and the root angle is mostly based on incisal mandibular plane angle, angulation, torque, and other angles in the traditional two-dimensional measurement. Fitting CBCT and digital model data can be used to monitor the relationship between root and alveolar bone during orthodontic treatment, but a more comprehensive, standardized three-dimensional tooth root position measurement method is required to make full use of the root data provided by CBCT to study the relative optimal position of the tooth root at different anatomical levels, which combines with computer technology to optimize the digital design of orthodontic diagnosis and treatment.

Progress on application of artificial intelligence technology in orthodontic diagnosis and treatment / 口腔疾病防治

@#In recent years, artificial intelligence technology has developed rapidly and has been gradually applied to the fields of clinical image data processing, auxiliary diagnosis and prognosis evaluation. Research has shown that it can simplify doctors’ clinical tasks, quickly provide analysis and processing results, and has high accuracy. In terms of orthodontic diagnosis and treatment, artificial intelligence can assist in the rapid fixation of two-dimensional and three-dimensional cephalometric measurements. In addition, it is also widely used in the efficient processing and analysis of three-dimensional dental molds data, and shows considerable advantages in determining deciding whether orthodontic treatment needs tooth extraction, thus assisting in judging the stage of growth and development, orthodontic prognosis and aesthetic evaluation. Although the application of artificial intelligence technology is limited by the quantity and quality of training data, combining it with orthodontic clinical diagnosis and treatment can provide faster and more effective analysis and diagnosis and support more accurate diagnosis and treatment decisions. This paper reviews the current application of artificial intelligence technology in orthodontic diagnosis and treatment in the hope that orthodontists can rationally treat and use artificial intelligence technology in the clinic, and make artificial intelligence better serve orthodontic clinical diagnosis and treatment, so as to promote the further development of intelligent orthodontic diagnosis and treatment processes.