A three-dimensional finite element model of premaxillary bone and anterior teeth was established with ANSYS 13.0. The anterior teeth were fixed with strong stainless labial archwire and lingual frame. In the horizontal loading experiments, a horizontal retraction force of 1.5 N was applied bilaterally to the segment through hooks at the same height between 7 and 21 mm from the incisal edge of central incisor; in vertical loading experiments, a vertical intrusion force of 1.5 N was applied at the midline of lingual frame with distance between 4 and 16 mm from the incisal edge of central incisor. After loading, solution was done and displacement and maximum principle stress were calculated. After horizontal loading, lingual displacement and stress in periodontal membrane (PDM) was most homogeneous when the traction force was 14 mm from the edge of central incisor; after vertical loading, intrusive displacement and stress in PDM were most homogeneous when the traction force was 12 mm from the incisal edge of central incisor. The results of this study suggested that the location of center of resistance (CRe) of six maxillary anterior teeth is about 14 mm gingivally and 12 mm lingually to incisal edge of central incisor. The location can provide evidence for theoretical and clinical study in orthodontics.
In the study of oral orthodontics, the dental tissue models play an important role in finite element analysis results. Currently, the commonly used alveolar bone models mainly have two kinds: the uniform and the non-uniform models. The material of the uniform model was defined with the whole alveolar bone, and each mesh element has a uniform mechanical property. While the material of the elements in non-uniform model was differently determined by the Hounsfield unit (HU) value of computed tomography (CT) images where the element was located. To investigate the effects of different alveolar bone models on the biomechanical responses of periodontal ligament (PDL), a clinical patient was chosen as the research object, his mandibular canine, PDL and two kinds of alveolar bone models were constructed, and intrusive force of 1 N and moment of 2 Nmm were exerted on the canine along its root direction, respectively, which were used to analyze the hydrostatic stress and the maximal logarithmic principal strain of PDL under different loads. Research results indicated that the mechanical responses of PDL had been affected by alveolar bone models, no matter the canine translation or rotation. Compared to the uniform model, if the alveolar bone was defined as the non-uniform model, the maximal stress and strain of PDL were decreased by 13.13% and 35.57%, respectively, when the canine translation along its root direction; while the maximal stress and strain of PDL were decreased by 19.55% and 35.64%, respectively, when the canine rotation along its root direction. The uniform alveolar bone model will induce orthodontists to choose a smaller orthodontic force. The non-uniform alveolar bone model can better reflect the differences of bone characteristics in the real alveolar bone, and more conducive to obtain accurate analysis results.
Evidence-based dentistry has been established for more than a decade, and described as ‘the conscientious, explicit and judicious use of current best evidence in making decisions about the care of individual patients＇. However, Orthodontic clinicians in China still tend to base their treatment protocols on the ‘it works in my hands＇evidence provided by their peers, mainly due to their weak experience in searching and applying clinical evidences. In this article, authors are willing to share their experience with their Chinese peers, and to promote the dissemination and application of evidence-based orthodontics in clinical practice.
Complete three-dimensional (3D) tooth model provides essential information to assist orthodontists for diagnosis and treatment planning. Currently, 3D tooth model is mainly obtained by segmentation and reconstruction from dental computed tomography (CT) images. However, the accuracy of 3D tooth model reconstructed from dental CT images is low and not applicable for invisalign design. And another serious problem also occurs,i.e. frequentative dental CT scan during different intervals of orthodontic treatment often leads to radiation to the patients. Hence, this paper proposed a method to reconstruct tooth model based on fusion of dental CT images and laser-scanned images. A complete 3D tooth model was reconstructed with the registration and fusion between the root reconstructed from dental CT images and the crown reconstructed from laser-scanned images. The crown of the complete 3D tooth model reconstructed with the proposed method has higher accuracy. Moreover, in order to reconstruct complete 3D tooth model of each orthodontic treatment interval, only one pre-treatment CT scan is needed and in the orthodontic treatment process only the laser-scan is required. Therefore, radiation to the patients can be reduced significantly.
To address the issues of time-consuming manual tooth alignment, reliance on physician experience, and insufficient structural constraints in automated methods in traditional orthodontic treatment planning, this paper proposes an automated tooth alignment network model that integrates multi-source geometric information learning. First, this study uses tooth mesh data as input to construct a graph convolution-based network for extracting tooth geometric features, fully utilizing the topological connectivity and local geometric features of the tooth model to enhance the extraction of tooth morphological features. Then, an arch line prediction network is designed to guide the teeth to align orderly along the natural arch line by explicitly modeling the tooth alignment trajectory, thereby ensuring the anatomical rationality and aesthetics of the overall structure. Based on this, this study designs collision avoidance loss and arch alignment loss to reduce geometric conflicts between adjacent teeth and constrain the overall posture, making the alignment results more stable and realistic. To verify the effectiveness of the method, validation experiments were conducted on multiple sets of real dental arch data, and comparisons were made with mainstream methods such as tooth alignment network (TaligNet), parameterized spatial transformation network (PSTN), and Transformer-based network for tooth alignment (TANet). The results show that the proposed method improves the evaluation metrics to varying degrees, fully integrates global and local geometric information, improves tooth posture coordination and alignment smoothness, and provides efficient and standardized intelligent support for digital orthodontic treatment planning.