Automatic recognition of landmarks on digital dental models

Brénainn Woodsend, Eirini Koufoudaki, Peter A. Mossey, Ping Lin (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

Abstract

Fundamental principle in improving Dental and Orthodontic treatments is the ability to quantitatively assess and cross-compare their outcomes. Such assessments require calculating distances and angles from 3D coordinates of dental landmarks. The costly and repetitive task of hand-labelling dental models hinder studies requiring large sample size to penetrate statistical noise.

We have developed techniques and a software implementing these techniques to map out automatically, 3D dental scans. This process is divided into consecutive steps - determining a model's orientation, separating and identifying the individual tooth and finding landmarks on each tooth - described in this paper. The examples to demonstrate the techniques, software and discussions on remaining issues are provided as well. The software is originally designed to automate Modified Huddard Bodemham (MHB) landmarking for assessing cleft lip/palate patients. Currently only MHB landmarks are supported, however it is extendable to any predetermined landmarks.

The software, coupled with intra-oral scanning innovation, should supersede the arduous and error prone plaster model and calipers approach to Dental research, and provide a stepping-stone towards automation of routine clinical assessments such as "index of orthodontic treatment need" (IOTN).

Original languageEnglish
Article number104819
Number of pages17
JournalComputers in Biology and Medicine
Volume137
Early online date2 Sep 2021
DOIs
Publication statusPublished - Oct 2021

Keywords

  • 3D analysis
  • Artificial intelligence
  • Automation
  • Dental
  • Landmarks

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