Morphometric analysis of palatal rugae in different malocclusions

The Facial Characteristics of Individuals with Posterior Crossbite: A Cross-Sectional Study

Facial morphology is known to be influenced by genetic and environmental factors. Scientific evidence regarding facial parameters in patients with posterior crossbite is lacking. This study aimed to investigate the association between posterior crossbite and facial parameters. This cross-sectional study included 34 adolescents with and 34 adolescents without posterior crossbite in the age range from 13 to 15 years. Facial surface scans were acquired with a 3dMD imaging system, and landmark-based analysis was performed. Data were analyzed using the Mann-Whitney U test and Spearman's correlations. Individuals in the control group had lower face heights (females: p = 0.003, r = 0.45; males: p = 0.005, r = 0.57). The control group females presented with smaller intercanthal width (p = 0.04; r = 0.31) and anatomical nose width (p = 0.004; r = 0.43) compared with the crossbite group females. The males in the control group had wider nostrils. In the control group, significant correlations among different facial parameters were more common, including the correlations between eye width and other transversal face measurements. On the contrary, the facial width was correlated with nasal protrusion (r = 0.657; p < 0.01) and the morphological width of the nose (r = 0.505; p < 0.05) in the crossbite group alone. In both groups, the philtrum width was linked with the anatomical and morphological widths of the nose. Conclusions: Patients with posterior crossbites have increased face height and different patterns of facial proportions compared with individuals without crossbites.

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning

OBJECTIVES

Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate.

SETTING AND SAMPLE POPULATION

This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals.

MATERIALS AND METHODS

Principal component analysis (PCA) and autoencoders (AE) are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity.

RESULTS

We found that the SAE obtains equivalent results to PCA and AEs for all evaluation metrics. SAE scores were found to be uncorrelated and provided an optimally compact representation of the shapes.

CONCLUSION

We conclude that the SAE is a promising tool for 3D palatal shape analysis, which effectively combines the power of PCA with the flexibility of deep learning. This opens future AI driven applications of shape analysis in orthodontics and other related clinical disciplines.