Three-dimensional evaluation of facial morphology in children aged 5-6 years with a Class III malocclusion

Occlusion-Based Three-Dimensional Craniofacial Anthropometric and Symmetric Evaluation in Preadolescences: A Comparative COHORT Study

BACKGROUND

The importance of early diagnosis of pediatric malocclusion and early intervention has been emphasized. Without use of radiation, 3D imaging holds the potential to be an alternative for evaluating facial features in school-aged populations.

METHODS

Students aged 9 and 10 years were recruited. We performed annual 3D stereophotogrammetry of the participants' heads. A total of 37 recognizable anatomical landmarks were identified for linear, angular, and asymmetric analyses using the MATLAB program.

RESULTS

This study included 139 healthy Taiwanese children with a mean age of 9.13, of whom 74 had class I occlusion, 50 had class II malocclusion, and 15 had class III malocclusion. The class III group had lower soft-tissue convexity (p = 0.01) than the class II group. The boys with class II malocclusion had greater dimensions in the anteroposterior position of the mid-face (p = 0.024) at age 10. Overall asymmetry showed no significance (p > 0.05). Heat maps of the 3D models exhibited asymmetry in the mid-face of the class II group and in the lower face of the class III group.

CONCLUSION

Various types of malocclusion exhibited distinct facial traits in preadolescents. Those with class II malocclusion had a protruded maxilla and convex facial profile, whereas those with class III malocclusion had a less convex facial profile. Asymmetry was noted in facial areas with relatively prominent soft-tissue features among different malocclusion types.

An exploration of adolescent facial shape changes with age via multilevel partial least squares regression

BACKGROUND AND OBJECTIVES

Multilevel statistical models represent the existence of hierarchies or clustering within populations of subjects (or shapes in this work). This is a distinct advantage over single-level methods that do not. Multilevel partial-least squares regression (mPLSR) is used here to study facial shape changes with age during adolescence in Welsh and Finnish samples comprising males and females.

METHODS

3D facial images were obtained for Welsh and Finnish male and female subjects at multiple ages from 12 to 17 years old. 1000 3D points were defined regularly for each shape by using "meshmonk" software. A three-level model was used here, including level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The mathematical formalism of mPLSR is given in an Appendix.

RESULTS

Differences in facial shape between the ages of 12 and 17 predicted by mPLSR agree well with previous results of multilevel principal components analysis (mPCA); buccal fat is reduced with increasing age and features such as the nose, brow, and chin become larger and more distinct. Differences due to ethnicity and sex are also observed. Plausible simulated faces are predicted from the model for different ages, sexes and ethnicities. Our models provide good representations of the shape data by consideration of appropriate measures of model fit (RMSE and R2).

CONCLUSIONS

Repeat measures in our dataset for the same subject at different ages can only be modelled indirectly at the lowest level of the model at discrete ages via mPCA. By contrast, mPLSR models age explicitly as a continuous covariate, which is a strong advantage of mPLSR over mPCA. These investigations demonstrate that multivariate multilevel methods such as mPLSR can be used to describe such age-related changes for dense 3D point data. mPLSR might be of much use in future for the prediction of facial shapes for missing persons at specific ages or for simulating shapes for syndromes that affect facial shape in new subject populations.

Multilevel principal components analysis of three-dimensional facial growth in adolescents

BACKGROUND AND OBJECTIVES

The study of age-related facial shape changes across different populations and sexes requires new multivariate tools to disentangle different sources of variations present in 3D facial images. Here we wish to use a multivariate technique called multilevel principal components analysis (mPCA) to study three-dimensional facial growth in adolescents.

METHODS

These facial shapes were captured for Welsh and Finnish subjects (both male and female) at multiple ages from 12 to 17 years old (i.e., repeated-measures data). 1000 "dense" 3D points were defined regularly for each shape by using a deformable template via "meshmonk" software. A three-level model was used here, namely: level 1 (sex/ethnicity); level 2, all "subject" variations excluding sex, ethnicity, and age; and level 3, age. The technicalities underpinning the mPCA method are presented in Appendices.

RESULTS

Eigenvalues via mPCA predicted that: level 1 (ethnicity/sex) contained 7.9% of variation; level 2 contained 71.5%; and level 3 (age) contained 20.6%. The results for the eigenvalues via mPCA followed a similar pattern to those results of single-level PCA. Results for modes of variation made sense, where effects due to ethnicity, sex, and age were reflected in modes at appropriate levels of the model. Standardised scores at level 1 via mPCA showed much stronger differentiation between sex and ethnicity groups than results of single-level PCA. Results for standardised scores from both single-level PCA and mPCA at level 3 indicated that females had different average "trajectories" with respect to these scores than males, which suggests that facial shape matures in different ways for males and females. No strong evidence of differences in growth patterns between Finnish and Welsh subjects was observed.

CONCLUSIONS

mPCA results agree with existing research relating to the general process of facial changes in adolescents with respect to age quoted in the literature. They support previous evidence that suggests that males demonstrate larger changes and for a longer period of time compared to females, especially in the lower third of the face. These calculations are therefore an excellent initial test that multivariate multilevel methods such as mPCA can be used to describe such age-related changes for "dense" 3D point data.

Differential Diagnosis of Skeletal Class III

Skeletal Class III is a relatively rare malocclusion of the craniofacial complex and the accurate differential diagnosis of its aetiology is necessary so that it may be correctly treated. Differential diagnosis of Class III aetiopathogenesis should distinguish between: a) true skeletal Class III as opposed to pseudo Class III; b) three forms of Skeletal Class III, in which there is either maxillary deficiency only or mandibular excess only or combination of both; and c) skeletal Class III that may be treated with orthodontic treatment alone, as opposed to Class III that is difficult to manage with orthodontic treatment alone and requires combine orthodontic and surgical approach. Differential diagnosis is mainly based on clinical examination and cephalometric analysis. The aim of this paper is to present the basic principles and modes of achieving differential diagnosis in skeletal Class III cases.

What's in a Smile? Initial Analyses of Dynamic Changes in Facial Shape and Appearance

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?”.

A three-dimensional soft tissue analysis of Class III malocclusion: a case-controlled cross-sectional study

OBJECTIVE

The present study used the optical surface laser scanning technique to compare the facial features of patients aged 8-18 years presenting with Class I and Class III incisor relationship in a case-control design.

MATERIALS AND METHODS

Subjects with a Class III incisor relationship, aged 8-18 years, were age and gender matched with Class I control and underwent a 3-dimensional (3-D) optical surface scan of the facial soft tissues.

RESULTS

Landmark analysis revealed Class III subjects displayed greater mean dimensions compared to the control group most notably between the ages of 8-10 and 17-18 years in both males and females, in respect of antero-posterior (P = 0.01) and vertical (P = 0.006) facial dimensions. Surface-based analysis, revealed the greatest difference in the lower facial region, followed by the mid-face, whilst the upper face remained fairly consistent.

CONCLUSION

Significant detectable differences were found in the surface facial features of developing Class III subjects.

Three-Dimensional Evaluation of the Upper Airway Morphological Changes in Growing Patients with Skeletal Class III Malocclusion Treated by Protraction Headgear and Rapid Palatal Expansion: A Comparative Research

Objective

The aim of this study was to evaluate the morphological changes of upper airway after protraction headgear and rapid maxillary expansion (PE) treatment in growing patients with Class III malocclusion and maxillary skeletal deficiency compared with untreated Class III patients by cone-beam computed tomography (CBCT).

Methods

Thirty growing patients who have completed PE therapy were included in PE group. The control group (n = 30) was selected from the growing untreated patients with the same diagnosis. The CBCT scans of the pre-treatment (T1) and post-treatment (T2) of PE group and the control group were collected. Reconstruction and registration of the 3D models of T1 and T2 were completed. By comparing the data obtained from T1, T2 and control group, the morphological changes of the upper airway during the PE treatment were evaluated.

Results

Comparing with the data from T1 group, the subspinale (A) of maxilla and the upper incisor (UI) of the T2 group were moved in the anterior direction. The gnathion (Gn) of mandible was moved in the posterior-inferior direction. The displacement of the hyoid bone as well as the length and width of dental arch showed significant difference. The volume and mean cross-sectional area of nasopharynx, velopharynx and glossopharynx region showed significant difference. The largest anteroposterior/the largest lateral (AP/LR) ratios of the velopharynx and glossopharynx were increased, but the AP/LR ratio of the hypopharynx was decreased. In addition, the length and width of the maxillary dental arch, the displacement of the hyoid bone, the volume of nasopharynx and velopharynx, and the AP/LR ratio of the hypopharynx and velopharynx showed significant difference between the data from control and T2 group.

Conclusion

The PE treatment of Class Ⅲ malocclusion with maxillary skeletal hypoplasia leads to a significant increase in the volume of nasopharynx and velopharynx.

Diagnosis of Class III malocclusion in 7- to 8-year-old children--a 3D evaluation

OBJECTIVES

The aim of this study was to characterize facial and jaw morphology of children with Class III malocclusion in early mixed dentition.

METHODS

This study was conducted on 7- to 8-year-old Caucasian children, 48 children with Class III malocclusion and 91 children with normal occlusion. Surface images of faces and study casts were obtained using laser scanning. Two average facial templates were constructed for the males and females in the control group. The facial images were superimposed on the corresponding average templates. Facial parameters, palatal volumes, and gingival surface areas were measured and group differences were quantified. The analysis of variance was used for statistical evaluation of the measured parameters.

RESULTS

The results revealed shorter lower face height (P < 0.001), concave facial profile (P < 0.001), retruded maxilla (P < 0.001), protruded mandible (P < 0.001), retrusive mid-face restricted area (P < 0.001), reduced gingival surface area of the maxilla (P = 0.013), and reduced maxilla/mandible gingival surface area ratio (P < 0.001) in the Class III group compared to the control group. There were no differences between the groups in upper face height, restricted areas of the upper and lower face, palatal volume, and gingival surface area of the mandible (P > 0.05).

LIMITATIONS

Regardless of the fact that the prevalence of Class III malocclusion is rather small, the sample size could be larger.

CONCLUSIONS

Class III subjects show clinically relevant facial and jaws characteristics in pre-pubertal growth period. A comprehensive diagnosis should include transverse dimension analysis.