Relationship between the maxillofacial skeletal pattern and the morphology of the mandibular symphysis: Structural equation modeling.

OBJECTIVE: The purpose of this study was to investigate the relationship between the facial skeletal patterns and the shape of the mandibular symphysis in adults with malocclusion by using a structural equation model (SEM). METHODS: Ninety adults who had malocclusion and had records of facial skeletal measurements performed using cone-beam computed tomography were selected for this study. The skeletal measurements were classified into three groups (vertical, anteroposterior, and transverse). Cross-sectional images of the mandibular symphysis were analyzed using generalized Procrustes and principal component (PC) analyses. A SEM was constructed after the factors were extracted via factor analysis. RESULTS: Two factors were extracted from the transverse, vertical, and anteroposterior skeletal measurements. Latent variables were extracted for each factor. PC1, PC2, and PC3 were selected to analyze the variations of the mandibular symphyseal shape. The SEM was constructed using the skeletal variables, PCs, and latent variables. The SEM showed that the vertical latent variable exerted the most influence on the mandibular symphyseal shape. CONCLUSIONS: The relationship between the skeletal pattern and the mandibular symphysis was analyzed using a SEM, which showed that the vertical facial skeletal pattern had the highest effect on the shape of the mandibular symphysis.

Analysis of the relationship between the morphology of the palate and facial skeletal patterns in Class III malocclusion using structural equation modelling.

INTRODUCTION: The present study investigated the relationship between facial skeletal patterns and morphology of the palate in adult patients with Class III malocclusion using structural equation modelling (SEM). SETTING AND SAMPLE POPULATION: One hundred cone beam computed tomography images of Class III adults were evaluated for skeletal measurements. MATERIALS AND METHODS: The skeletal measurements were classified into the vertical, anteroposterior and transverse group based on factor analysis. 3D scanning model of the maxilla was analysed by Generalized procrustes analysis (GPA) and principal component analysis (PCA). Structural equation modelling was used to analyse relationship among the skeletal and morphometric factors. RESULTS: According to the factor analysis, latent variables were extracted by each skeletal variable. First principal component (PC1) and PC2 of palatal morphology were used to analyse relationship with skeletal variables. As results of the structural equation model, the transverse latent variable had the most influence on PC1, followed by vertical and anteroposterior variables. This result means that as the facial width increases, the palate becomes narrower, deeper and longer. CONCLUSIONS: The relationship between the skeletal pattern with Class III malocclusion and palatal morphology was analysed through SEM. The transverse facial skeletal pattern showed the highest correlation with PC1 of palatal morphology.

Correlation between the cross-sectional morphology of the mandible and the three-dimensional facial skeletal pattern: A structural equation modeling approach.

OBJECTIVE: To clarify the relationship between the cross-sectional morphology of the mandible and vertical, transverse, and anteroposterior facial skeletal patterns using statistical shape analysis and structural equation modeling (SEM). MATERIALS AND METHODS: We used 150 cone beam computed tomography (CBCT) images to obtain three-dimensional (3D) facial landmarks and cross-sectional images of the mandible. The morphology of the inner and outer cortices of the mandible was analyzed using statistical shape analysis, including generalized Procrustes analysis and principal component analysis (PCA). Factor analysis was performed to determine factors pertaining to the skeletal measurements and shape variations for the inner and outer cortices, following which a structural equation model was constructed. RESULTS: Using factor analysis, characteristics of the vertical, transverse, and anteroposterior facial skeletal patterns were determined. PCA of the cross-sectional morphology of the mandible revealed 70% of the cumulative proportion by PC1 and PC2 after generalized Procrustes superimpositions. SEM showed complex relationships between the facial skeletal patterns and variations in the cross-sectional morphology of the mandibular cortices. The influence of the transverse factors on the outer cortex as a latent variable was relatively significant ( P = .057). However, the influence of the vertical factors on the outer and inner cortices was not significant. CONCLUSIONS: The transverse skeletal pattern is associated with the morphology of the outer cortex of the mandible.