Upper Airway Changes after Orthodontic Extraction Treatment in Adults: A Preliminary Study using Cone Beam Computed Tomography

<div><p>Objective</p><p>Whether the orthodontic treatment with premolar extraction and maximum anchorage in adults will lead to a narrowed upper airway remains under debated. The study aims to investigate the airway changes after orthodontic extraction treatment in adult patients with Class II and hyperdivergent skeletal malocclusion.</p><p>Materials and Methods</p><p>This retrospective study enrolled 18 adults with Class II and hyperdivergent skeletal malocclusion (5 males and 13 females, 24.1 ± 3.8 years of age, BMI 20.33 ± 1.77 kg/m²). And 18 untreated controls were matched 1:1 with the treated patients for age, sex, BMI, and skeletal pattern. CBCT images before and after treatment were obtained. DOLPHIN 11.7 software was used to reconstruct and measure the airway size, hyoid position, and craniofacial structures. Changes in the airway and craniofacial parameters from pre to post treatment were assessed by Wilcoxon signed rank test. Mann-Whitney U test was used in comparisons of the airway parameters between the treated patients and the untreated controls. Significant level was set at 0.05.</p><p>Results</p><p>The upper and lower incisors retracted 7.87 mm and 6.10 mm based on the measurement of U1-VRL and L1-VRL (P < 0.01), while the positions of the upper and lower molars (U6-VRL, and L6-VRL) remained stable. Volume, height, and cross-sectional area of the airway were not significantly changed after treatment, while the sagittal dimensions of SPP-SPPW, U-MPW, PAS, and V-LPW were significantly decreased (P < 0.05), and the morphology of the cross sections passing through SPP-SPPW, U-MPW, PAS, and V-LPW became anteroposteriorly compressed (P <0.001). No significant differences in the airway volume, height, and cross-sectional area were found between the treated patients and untreated controls.</p><p>Conclusions</p><p>The airway changes after orthodontic treatment with premolar extraction and maximum anchorage in adults are mainly morphological changes with anteroposterior dimension compressed in airway cross sections, rather than a decrease in size.</p></div

DataSheet1_Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment.docx

Objectives: Machine learning is increasingly being used in the medical field. Based on machine learning models, the present study aims to improve the prediction performance of craniodentofacial morphological harmony judgment after orthodontic treatment and to determine the most significant factors.Methods: A dataset of 180 subjects was randomly selected from a large sample of 3,706 finished orthodontic cases from six top orthodontic treatment centers around China. Thirteen algorithms were used to predict the value of the cephalometric morphological harmony score of each subject and to search for the optimal model. Based on the feature importance ranking and by removing features, the regression models of machine learning (including the Adaboost, ExtraTree, XGBoost, and linear regression models) were used to predict and compare the score of harmony for each subject from the dataset with cross validations. By analyzing the prediction values, the most optimal model and the most significant cephalometric characteristics were determined.Results: When nine features were included, the performance of the XGBoost regression model was MAE = 0.267, RMSE = 0.341, and Pearson correlation coefficient = 0.683, which indicated that the XGBoost regression model exhibited the best fitting and predicting performance for craniodentofacial morphological harmony judgment. Nine cephalometric features including L1/NB (inclination of the lower central incisors), ANB (sagittal position between the maxilla and mandible), LL-EP (distance from the point of the prominence of the lower lip to the aesthetic plane), SN/OP (inclination of the occlusal plane), SNB (sagittal position of the mandible in relation to the cranial base), U1/SN (inclination of the upper incisors to the cranial base), L1-NB (protrusion of the lower central incisors), Ns-Prn-Pos (nasal protrusion), and U1/L1 (relationship between the protrusions of the upper and lower central incisors) were revealed to significantly influence the judgment.Conclusion: The application of the XGBoost regression model enhanced the predictive ability regarding the craniodentofacial morphological harmony evaluation by experts after orthodontic treatment. Teeth position, teeth alignment, jaw position, and soft tissue morphology would be the most significant factors influencing the judgment. The methodology also provided guidance for the application of machine learning models to resolve medical problems characterized by limited sample size.</p

DataSheet2_Machine Learning-Based Evaluation on Craniodentofacial Morphological Harmony of Patients After Orthodontic Treatment.docx

Objectives: Machine learning is increasingly being used in the medical field. Based on machine learning models, the present study aims to improve the prediction performance of craniodentofacial morphological harmony judgment after orthodontic treatment and to determine the most significant factors.Methods: A dataset of 180 subjects was randomly selected from a large sample of 3,706 finished orthodontic cases from six top orthodontic treatment centers around China. Thirteen algorithms were used to predict the value of the cephalometric morphological harmony score of each subject and to search for the optimal model. Based on the feature importance ranking and by removing features, the regression models of machine learning (including the Adaboost, ExtraTree, XGBoost, and linear regression models) were used to predict and compare the score of harmony for each subject from the dataset with cross validations. By analyzing the prediction values, the most optimal model and the most significant cephalometric characteristics were determined.Results: When nine features were included, the performance of the XGBoost regression model was MAE = 0.267, RMSE = 0.341, and Pearson correlation coefficient = 0.683, which indicated that the XGBoost regression model exhibited the best fitting and predicting performance for craniodentofacial morphological harmony judgment. Nine cephalometric features including L1/NB (inclination of the lower central incisors), ANB (sagittal position between the maxilla and mandible), LL-EP (distance from the point of the prominence of the lower lip to the aesthetic plane), SN/OP (inclination of the occlusal plane), SNB (sagittal position of the mandible in relation to the cranial base), U1/SN (inclination of the upper incisors to the cranial base), L1-NB (protrusion of the lower central incisors), Ns-Prn-Pos (nasal protrusion), and U1/L1 (relationship between the protrusions of the upper and lower central incisors) were revealed to significantly influence the judgment.Conclusion: The application of the XGBoost regression model enhanced the predictive ability regarding the craniodentofacial morphological harmony evaluation by experts after orthodontic treatment. Teeth position, teeth alignment, jaw position, and soft tissue morphology would be the most significant factors influencing the judgment. The methodology also provided guidance for the application of machine learning models to resolve medical problems characterized by limited sample size.</p

Airway measurements of the volume (V), height (H), and minimum cross sectional area (Min) using Dolphin 11.7 software package.

<p>(A). Pink area defines the airway portion of interest, and the green plane locates the minimum cross sectional area. V, H, and Min were automatically calculated; (B). Front view of the evaluated upper airway. The airway was divided into nasopharynx, velopharynx, and hypopharynx by two horizontal planes passing the posterior nasal spine and the tip of the soft palate; (C). Lateral view of the evaluated upper airway.</p

Changes in the volume, height, and cross sectional area of the upper airway after orthodontic extraction treatment in adults with Class II and hyperdivergent pattern (n = 18).

<p>* P<0.05</p><p>** P<0.01</p><p>^a Wilcoxon signed rank test</p><p>Changes in the volume, height, and cross sectional area of the upper airway after orthodontic extraction treatment in adults with Class II and hyperdivergent pattern (n = 18).</p

Changes in the craniofacial structures and position of the hyoid bone after orthodontic extraction treatment in adults with Class II and hyperdivergent pattern (n = 18).

<p>* P<0.05</p><p>** P<0.01</p><p>^a Wilcoxon signed rank test</p><p>Changes in the craniofacial structures and position of the hyoid bone after orthodontic extraction treatment in adults with Class II and hyperdivergent pattern (n = 18).</p

Measurements of the craniofacial structures and hyoid position in the lateral cephalograms generated by CBCT.

<p>(1) A-VRL; (2) B-VRL; (3) U1-VRL; (4) L1-VRL; (5) H-MP; (6) H-C3; (7) H-Rgn; and (8) H-HRL.</p

Measurements of the area and morphology of the cross-sectional planes passing the sagittal linear measurements.

<p>(A) Pink area defines the upper airway, and the yellow line indicates the plane passing the sagittal airway parameter of U-MPW. (B) Coronal view of the cross section passing the U-MPW. The A-P dimeter, lateral dimeter, and area are measured. (C) and (D) showed the typical changes of the morphology from pre to post treatment in the same cross section passing U-MPW.</p

Measurements of the sagittal airway dimension, craniofacial structures, and hyoid position.

<p>Measurements of the sagittal airway dimension, craniofacial structures, and hyoid position.</p

Differences in the volume, height, and cross sectional area of the upper airway between the post-treatment adult patients and the matched untreated controls.

<p>^a Mann-Whitney U test</p><p>Differences in the volume, height, and cross sectional area of the upper airway between the post-treatment adult patients and the matched untreated controls.</p