Shape variation and covariation of upper and lower dental arches of an orthodontic population

Papagiannis, Alexandros; Halazonetis, Demetrios J.

doi:10.1093/ejo/cjv019

Cited by 28 publications

(20 citation statements)

References 62 publications

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“…The calculations were carried out using Viewbox 4 software. 6 Method error was assessed by repeated digitization of 20 randomly selected study casts by the same investigator after a period of 10 days.…”

Section: Discussionmentioning

confidence: 99%

“…4 Early diagnosis is very important, and interceptive orthodontic treatment could not only improve skeletal relationship and eliminate functional interferences but also may correct disturbances during the eruption. 5 Geometric morphometrics has been proposed in the literature 6 as an original method of visualization of shape changes. It can show three-dimensional (3D) morphological changes in their complexity, in a more effective way than traditional bidimensional measurements used for morphometric analyses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Geometric morphometric analysis of the palatal morphology in children with impacted incisors: A three-dimensional evaluation

Pavoni

Paoloni

Ghislanzoni

et al. 2016

The Angle Orthodontist

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Geometric morphometric analysis of the palatal morphology in children with impacted incisors: A three-dimensional evaluation

Pavoni

Paoloni

Ghislanzoni

et al. 2016

The Angle Orthodontist

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“…Like the skeletal measurement, analysing the palatal shape through 3D laser scanning has aided in high‐speed measurement with high accuracy . The morphological variations are visualized and analysed by geometric morphometric analysis …”

Section: Introductionmentioning

confidence: 99%

“…9 The morphological variations are visualized and analysed by geometric morphometric analysis. 10,11 Structural equation modelling (SEM) can be used to analyse the relationship between various factors from the skeletal pattern and palatal morphology. This statistical technique simplify the relationship of multiple factors and make it possible to express the results in a schematic manner to help achieve an overall understanding.…”

Section: Introductionmentioning

confidence: 99%

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

Ahn

Shin

Choi

et al. 2019

Orthod Craniofacial Res

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Structured Abstract 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.

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“…For example, dental casts trimmed based on the patient’s bite registrations reproduce the patient’s pretreatment occlusion and are routinely taken for orthodontic diagnosis and treatment planning. Dental casts have been used in cross-sectional and longitudinal studies to document 3-dimensional (3D) variations via linear measurements of arch width, 15 perimeter and length, 16,17 and arch-shape variations 18 as well as other malocclusion indicators such as overjet, overbite, 19 curve of Spee, 20 tooth size-arch length discrepancies, 21 and various indexes to measure crowding and other dental irregularities. 22 …”

mentioning

confidence: 99%

Candidate gene analyses of 3-dimensional dentoalveolar phenotypes in subjects with malocclusion

Weaver

Miller

Fontoura

et al. 2017

American Journal of Orthodontics and Dentofacial Orthopedics

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Introduction Genetic studies of malocclusion etiology have identified 4 deleterious mutations in genes, DUSP6, ARHGAP21, FGF23, and ADAMTS1 in familial Class III cases. Although these variants may have large impacts on Class III phenotypic expression, their low frequency (<1%) makes them unlikely to explain most malocclusions. Thus, much of the genetic variation underlying the dentofacial phenotypic variation associated with malocclusion remains unknown. In this study, we evaluated associations between common genetic variations in craniofacial candidate genes and 3-dimensional dentoalveolar phenotypes in patients with malocclusion. Methods Pretreatment dental casts or cone-beam computed tomographic images from 300 healthy subjects were digitized with 48 landmarks. The 3-dimensional coordinate data were submitted to a geometric morphometric approach along with principal component analysis to generate continuous phenotypes including symmetric and asymmetric components of dentoalveolar shape variation, fluctuating asymmetry, and size. The subjects were genotyped for 222 single-nucleotide polymorphisms in 82 genes/loci, and phenotpye-genotype associations were tested via multivariate linear regression. Results Principal component analysis of symmetric variation identified 4 components that explained 68% of the total variance and depicted anteroposterior, vertical, and transverse dentoalveolar discrepancies. Suggestive associations (P < 0.05) were identified with PITX2, SNAI3, 11q22.2-q22.3, 4p16.1, ISL1, and FGF8. Principal component analysis for asymmetric variations identified 4 components that explained 51% of the total variations and captured left-to-right discrepancies resulting in midline deviations, unilateral crossbites, and ectopic eruptions. Suggestive associations were found with TBX1 AJUBA, SNAI3 SATB2, TP63, and 1p22.1. Fluctuating asymmetry was associated with BMP3 and LATS1. Associations for SATB2 and BMP3 with asymmetric variations remained significant after the Bonferroni correction (P <0.00022). Suggestive associations were found for centroid size, a proxy for dentoalveolar size variation with 4p16.1 and SNAI1. Conclusions Specific genetic pathways associated with 3-dimensional dentoalveolar phenotypic variation in malocclusions were identified.

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Shape variation and covariation of upper and lower dental arches of an orthodontic population

Abstract: Covariation patterns showed that the dental arches were integrated in width and depth. Integration in the vertical dimension was weak, mainly restricted to maxillary canine position.

Cited by 28 publications

References 62 publications

Geometric morphometric analysis of the palatal morphology in children with impacted incisors: A three-dimensional evaluation

Geometric morphometric analysis of the palatal morphology in children with impacted incisors: A three-dimensional evaluation

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

Candidate gene analyses of 3-dimensional dentoalveolar phenotypes in subjects with malocclusion

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