Automated landmarking for palatal shape analysis using geometric deep learning.

ABSTRACT:

Objectives

To develop and evaluate a geometric deep-learning network to automatically place seven palatal landmarks on digitized maxillary dental casts.

Settings and sample population

The sample comprised individuals with permanent dentition of various ethnicities. The network was trained from manual landmark annotations on 732 dental casts and evaluated on 104 dental casts.

Materials and methods

A geometric deep-learning network was developed to hierarchically learn features from point-clouds representing the 3D surface of each cast. These features predict the locations of seven palatal landmarks.

Results

Repeat-measurement reliability was <0.3 mm for all landmarks on all casts. Accuracy is promising. The proportion of test subjects with errors less than 2 mm

SUBMITTER: Croquet B 

PROVIDER: S-EPMC8818261 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature