Automated landmarking for palatal shape analysis using geometric deep learning

Balder Croquet, Harold Matthews, Jules Mertens, Yi Fan, Nele Nauwelaers, Soha Mahdi, Hanne Hoskens, Ahmed El Sergani, Tianmin Xu, Dirk Vandermeulen, Michael Bronstein, Mary Marazita, Seth Weinberg, Peter Claes*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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 was between 0.93 and 0.68, depending on the landmark. Unusually shaped and large palates generate the highest errors. There was no evidence for a difference in mean palatal shape estimated from manual compared to the automatic landmarking. The automatic landmarking reduces sample variation around the mean and reduces measurements of palatal size. Conclusions: The automatic landmarking method shows excellent repeatability and promising accuracy, which can streamline patient assessment and research studies. However, landmark indications should be subject to visual quality control.

Original languageEnglish
Pages (from-to)144-152
Number of pages9
JournalOrthodontics and Craniofacial Research
Volume24
Issue numberS2
DOIs
StatePublished - Dec 2021
Externally publishedYes

Funding

FundersFunder number
Research Foundation Flanders (Belgium)
National Institutes of Health
National Institute of Dental and Craniofacial ResearchR01DE016148
Fonds Wetenschappelijk OnderzoekISB0121N, G078518N
KU LeuvenC14/15/081 & C14/20/081

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