The aim of this study was to clarify the effect of the play between the bracket and the archwire on anterior tooth movement subjected to the retraction force from various lengths of power arms in sliding mechanics. A three-dimensional finite element method was used to simulate en masse anterior tooth retraction in sliding mechanics. The displacements of the maxillary incisor and the archwire deformation were calculated when the retraction force was applied. When a play did not exist, bodily movement was obtained at 5.0 mm length of power arm. In case a play existed, bodily movement was observed at the power arm length of 11.0 mm. In the actual clinical situation, a bracket/archwire play and the torsion of the archwire within the bracket slot should be taken into consideration to prescribe an optimal power arm length and to achieve effective anterior tooth movement.

Background: This study aimed to evaluate the effect of external force applied from mini-screws to the aligner during space closure in extraction cases, and to determine the optimal loading conditions to prevent lingual crown tipping of the incisor and mesial tipping of the molar using the finite element (FE) method. Methodology: A three-dimensional FE model of the maxillary dentition with extraction of first premolars was constructed, and three different loading conditions were designed. The aligner was activated for 0.25 mm of incisor retraction without using mini-screws, with application of 0, 100, 200, or 300 cN of distal force through mini-screws placed in the posterior region, or with application of 0, 100, 200, 300, or 400 cN of intrusive force through mini-screws placed in the anterior region, in addition to the application of 300 cN of distal force. Results: As the magnitude of distal force increased, the degree of mesial tipping of the first molar gradually decreased, becoming almost zero with nearly 300 cN of distal force. As the magnitude of intrusive force increased in addition to the application of 300 cN of distal force, the degree of lingual crown tipping of the central incisor gradually decreased, becoming almost zero with nearly 400 cN of intrusive force. Conclusion: Application of 300 cN of distal force to the aligner from mini-screws placed in the posterior region could completely prevent mesial tipping of the molar and anchorage loss during anterior retraction. Application of 400 cN of intrusive force from mini-screws placed in the anterior region could prevent lingual crown tipping of the incisor, thus providing better torque control for the incisors.

Background: More efficient molar distalization is demanded in orthodontics to shorten treatment times. In the present study, we propose a novel technique to improve force distribution to distalize molars more efficiently by using open-coil springs and an anchor screw. We conducted a finite element analysis to assess the efficiency of the proposed technique. Methods: A three-dimensional finite element model of an upper dental arch with brackets and an archwire was constructed based on cone-beam computed tomography. We analyzed two distalization methods: a conventional grouped distalization technique (NoSp model), and our proposed technique using open coils (Sp model). Finite element analyses were performed to evaluate long-term tooth movement in both techniques. Results: The distalization force was more evenly distributed in the Sp model than in the NoSp model. Moreover, less concentration of compressive stress in the periodontal ligament (PDL) was observed in the Sp model. The force systems of the two models became more similar as the distalization progressed. However, the NoSp model still showed higher stress concentration at the end of the simulation. Conclusions: Inserting open-coil springs between distalized teeth improved the distribution of the force significantly. The conventional grouped distalization method might cause stress concentration at the first premolar, indicating risks of the hyalinization of the PDL and root resorption.

Background/Objectives: Accurate prediction of craniofacial growth is essential for establishing orthodontic diagnoses and planning treatment. However, the ultimate extent of jaw growth remains largely judged subjectively based on clinical experience. In this study, we developed a convolutional neural network (CNN) model to predict chronological age from lateral cephalograms and to investigate whether artificial intelligence (AI) can autonomously learn growth-related morphological features. We also reevaluated which anatomical regions are most informative for predicting growth. Methods: We retrospectively analyzed 2116 cephalograms from patients 5–30 years old with malocclusion. After excluding patients with craniofacial syndromes or systemic diseases, 2014 images were used for training and 102 for testing. The mean age of the training dataset was 19.51 years (standard deviation [SD]: 4.71), whereas that of the test dataset was 18.20 years (SD: 7.33). In addition to the entire cephalograms, five regional datasets were generated (mandible, maxilla, cervical vertebrae, frontal region, and cranial base). All images were resized to 256 × 256 pixels and trained with a ResNet50-based CNN. Performance was evaluated using mean absolute error (MAE), Pearson’s correlation coefficient (r), and coefficient of determination (R²). To assess growth-related learning, test data were divided into growth (5–19 years) and post-growth (20–30 years) groups, and age trends were analyzed using a sliding window approach with an 8-year window. Results: The model trained on entire cephalograms achieved high accuracy in the younger group (MAE 1.16 years, r 0.952, R² 0.884), but performance declined markedly in the older group. Among the regional models, accuracy was highest for the mandible, followed by the cervical vertebrae and maxilla. Conclusions: The CNN model predicted chronological age with high accuracy, particularly in patients <20 years old, and may have captured age-associated craniofacial features related to growth. Prediction was most accurate in the mandible and cervical vertebrae—regions clinically used to assess growth. As a proof of concept, this approach may provide a foundation for future studies of craniofacial growth prediction through transfer learning, although further validation is required.