The predictability of dentoskeletal factors for soft-tissue chin strain during lip closure

Orthodontic incisor retraction caused changes in the soft tissue chin area: a retrospective study

BACKGROUND

To investigate the area and morphological changes around the soft tissue chin after orthodontic incisor retraction.

METHODS

Fifty-nine female adults with bimaxillary protrusion requiring extraction of four premolars were included in the study. Cephalograms were taken before (T0) and after (T1) orthodontic treatment. The soft tissue changes, including the area, thickness and morphology were measured. Paired-t tests were performed for statistical comparisons. Pearson correlation analyses and backward multivariate regression analyses were used to identify the relationship between the soft tissue changes and incisor retraction.

RESULTS

Following the incisor retractions (5.35 ± 1.79 mm and 4.42 ± 1.62 mm for the upper and lower, respectively), there was a significant increase in the soft tissue thickness of L1c-LL (0.64 ± 1.67 mm, P = 0.025) and Pog-Pog' (0.44 ± 1.10 mm, P = 0.022), and a significant decrease in the soft tissue thickness of B-B' (1.21 ± 1.34 mm, P <  0.01). Changes in the area of soft tissue chin and lower lip were not statistically significant (P > 0.05). Pearson coefficient between the thickness changes of B-B' and the retraction of lower incisors was - 0.376. The multiple correlations between the soft tissue thickness changes and incisor retractions were Y = 1.02-0.42a + 0.42b for L1c-LL, and Y = 0.17-0.31b for B-B'.

CONCLUSIONS

The orthodontic incisor retraction could cause soft tissue thickness changes (i.e. an increase in L1c-LL and Pog-Pog' and a decrease in B-B') without area changes.

Testing a better method of predicting postsurgery soft tissue response in Class II patients: A prospective study and validity assessment

OBJECTIVE

(1) To perform a prospective study using a new set of data to test the validity of a new soft tissue prediction method developed for Class II surgery patients and (2) to propose a better validation method that can be applied to a validation study.

MATERIALS AND METHODS

Subjects were composed of two subgroups: training subjects and validation subjects. Eighty Class II surgery patients provided the training data set that was used to build the prediction algorithm. The validation data set of 34 new patients was used for evaluating the prospective performance of the prediction algorithm. The validation was conducted using four validation methods: (1) simple validation and (2) fivefold, (3) 10-fold, and (4) leave-one-out cross-validation (LOO).

RESULTS

The characteristics between the training and validation subjects did not differ. The multivariate partial least squares regression returned more accurate prediction results than the conventional method did. During the prospective validation, all of the cross-validation methods (fivefold, 10-fold, and LOO) demonstrated fewer prediction errors and more stable results than the simple validation method did. No significant difference was noted among the three cross-validation methods themselves.

CONCLUSION

After conducting a prospective study using a new data set, this new prediction method again performed well. In addition, a cross-validation technique may be considered a better option than simple validation when constructing a prediction algorithm.

Influence of temporomandibular joint disc displacement on craniocervical posture and hyoid bone position

INTRODUCTION

The purpose of this study was to evaluate craniocervical posture and hyoid bone position in orthodontic patients with temporomandibular joint (TMJ) disc displacement.

METHODS

The subjects consisted of 170 female orthodontic patients who consented to bilateral magnetic resonance imaging of their TMJs. They were divided into 3 groups based on the results of magnetic resonance imaging of their TMJs: bilateral normal disc position, bilateral disc displacement with reduction, and bilateral disc displacement without reduction. Twenty-five variables from lateral cephalograms were analyzed with 1-way analysis of variance to investigate differences in craniocervical posture and hyoid bone position with respect to TMJ disc displacement status. Pearson correlation coefficients were calculated to analyze the relationships between craniofacial morphology and craniocervical posture or hyoid bone position.

RESULTS

Subjects with TMJ disc displacement were more likely to have an extended craniocervical posture with Class II hyperdivergent patterns. The most significant differences were found between patients with bilateral normal disc position and bilateral disc displacement without reduction. However, hyoid bone position in relation to craniofacial references was not significantly different among the TMJ disc displacement groups, except for variables related to the mandible. Pearson correlation coefficients indicated that extended craniocervical posture was significantly correlated with backward positioning and clockwise rotation of the mandible.

CONCLUSIONS

This suggests that craniocervical posture is significantly influenced by TMJ disc displacement, which may be associated with hyperdivergent skeletal patterns with a retrognathic mandible.

How to test validity in orthodontic research: a mixed dentition analysis example

INTRODUCTION

The data used to test the validity of a prediction method should be different from the data used to generate the prediction model. In this study, we explored whether an independent data set is mandatory for testing the validity of a new prediction method and how validity can be tested without independent new data.

METHODS

Several validation methods were compared in an example using the data from a mixed dentition analysis with a regression model. The validation errors of real mixed dentition analysis data and simulation data were analyzed for increasingly large data sets.

RESULTS

The validation results of both the real and the simulation studies demonstrated that the leave-1-out cross-validation method had the smallest errors. The largest errors occurred in the traditional simple validation method. The differences between the validation methods diminished as the sample size increased.

CONCLUSIONS

The leave-1-out cross-validation method seems to be an optimal validation method for improving the prediction accuracy in a data set with limited sample sizes.