Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients

Si Chen, Li Wang, Gang Li, Tai Hsien Wu, Shannon Diachina, Beatriz Tejera, Jane Jungeun Kwon, Feng Chang Lin, Yan Ting Lee, Tianmin Xu, Dinggang Shen, Ching Chang Ko

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Objectives: To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information. Materials and Methods: A machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) was used with cone-beam computed tomography (CBCT) images to quantify volumetric skeletal maxilla discrepancies of 30 study group (SG) patients with unilaterally impacted maxillary canines and 30 healthy control group (CG) subjects. Fully automatic segmentation was implemented for maxilla isolation, and maxillary volumetric and linear measurements were performed. Analysis of variance was used for statistical evaluation. Results: Maxillary structure was successfully auto-segmented, with an average dice ratio of 0.80 for three-dimensional image segmentations and a minimal mean difference of two voxels on the midsagittal plane for digitized landmarks between the manually identified and the machine learning–based (LINKS) methods. No significant difference in bone volume was found between impaction ([2.37 6 0.34] 3 104 mm3) and nonimpaction ([2.36 6 0.35] 3 104 mm3) sides of SG. The SG maxillae had significantly smaller volumes, widths, heights, and depths (P, .05) than CG. Conclusions: The data suggest that palatal expansion could be beneficial for those with unilateral canine impaction, as underdevelopment of the maxilla often accompanies that condition in the early teen years. Fast and efficient CBCT image segmentation will allow large clinical data sets to be analyzed effectively.

Original languageEnglish
Pages (from-to)77-84
Number of pages8
JournalAngle Orthodontist
Volume90
Issue number1
DOIs
Publication statusPublished - 2020 Jan
Externally publishedYes

Keywords

  • CBCT
  • Canine impaction
  • Image segmentation
  • Machine learning
  • Orthodontics

ASJC Scopus subject areas

  • Orthodontics

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