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

ABSTRACT:

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

SUBMITTER: Nauwelaers N 

PROVIDER: S-EPMC8830919 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature