Journal of the World Federation of Orthodontists最新文献

Introduction: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment outcomes and informed decision-making.

Materials and methods: A retrospective study was conducted using patient data from the University of Illinois Chicago Department of Orthodontics. Data included demographic, clinical, and radiographic information from 115 anterior open bite patients who successfully completed their treatment. ML algorithms, including random forest, support vector machine, k-nearest neighbor, and convolutional neural networks (CNN), were trained on a subset of the data to predict treatment outcomes.

Results: Significant differences were observed in the percentages of males and females between the extraction and nonextraction groups and cephalometric variables between the two groups, which include maxillary depth, maxillary height, SN-palatal plane, facial angle, facial axis-Ricketts, FMA, total facial height, lower facial height, SNA, SNB, and SN-MP e ML algorithms examined consisted of CNN2, CNN1, and Random Forest, which demonstrated the highest accuracy rates (∼83%), while k-Nearest Neighbor had the lowest (∼73%). Key features influencing accuracy included crowding, SN-palatal plane, SNA, FMA, molar relation, and facial height measurements.

Conclusions: The study's evaluation of AI algorithms showed that CNN2, CNN1, and random forest had an accuracy of approximately 83% in classifying extraction versus nonextraction cases. Notably, features such as U-crowding, L-crowding, SN-palatal plane, SNA, FMA, molar relation, total facial height, lower facial height, and facial axis-Ricketts were most influential in achieving accuracy rates comparable to traditional methods.

Background: The advances in technology have enabled the customization of appliances including mini-screw-assisted rapid palatal expansion (MARPE) appliances for skeletal expansion in young adult patients. The study assessed the short-term effects of customized MARPE appliances on the hard tissues, soft tissues, and airway volume over a period of 6 months.

Methods: A total of 15 patients in the age range of 15 to 25 years were treated for transverse maxillary deficiency using a three-dimensional (3D) printed customized MARPE appliance. The changes in hard tissues, soft tissues, and airway volume were evaluated using cone beam computed tomography before expansion (T0) and at 6 months post-expansion (T1). The Digital Imaging and Communications in Medicine files were analyzed for post-expansion changes using the NemoCeph 3D and 3D Slicer 5.6.1 software.

Results: An effective skeletal expansion was observed with significant changes in intercanine, interpremolar, and intermolar width; and decreased mid-palatal suture density in the anterior region (P < .05). The changes in tooth inclination and alveolar bone thickness were mostly non-significant apart from a significant decrease in buccal bone thickness in the coronal third region (P < .05). There was no significant root resorption or change in airway volumes (P > .05). The philtrum height increased significantly by 1.17 mm (P = .019) with no significant change on right and left sides.

Conclusions: Rapid palatal expansion with 3D-printed customized MARPE enables effective and symmetrical expansion with a significant increase in philtrum height and no significant adverse effects in terms of alveolar bone thickness, dental inclination, root resorption, and airway volumes.

Objective: To evaluate whether rapid palatal expansion (RPE) or miniscrew-assisted rapid palatal expansion (MARPE) affects nasal septum deviation (NSD).

Materials and methods: The study population includes 22 RPE patients ages 9.62 ± 1.38 years and 20 MARPE patients ages 19.38 ± 7.82 years with initial diagnostic cone-beam computed tomography (CBCT) scans (T0). Another CBCT scan (T1) was taken after patients underwent RPE or MARPE expansion treatment alone. NSD was evaluated three-dimensionally using a custom landmark analysis on T0 and T1 CBCT scans. Principal component analysis (PCA) and canonical variate analysis (CVA) were used to identify nasal septum shape differences before and after expansion treatment.

Results: PCA and CVA showed that while there was change in nasal septum shape from T0 to T1 for MARPE and RPE treatments, the general pattern in morphological change was not found when comparing the variety of phenotypes between individuals. The Procrustes ANOVA regression found P-values for MARPE centroid size and shape were 0.7861 and 1, and RPE centroid size and shape were 0.3508 and 1, respectively, suggesting that there were no significant differences in nasal septum size and shape following expansion. CVA found P-values were 0.99 for MARPE and 0.99 for RPE after 10,000 permutation tests for Procrustes distances, indicating that there were no significant differences between T0 and T1 group means for both treatment groups.

Conclusions: MARPE and RPE expansion treatments had no effect on nasal septum deviation from T0 to T1.