Development of intra-oral automated landmark recognition (ALR) for dental and occlusal outcome measurements

Brenainn Woodsend, Eirini Koufoudaki, Ping Lin, Grant McIntyre, Ahmed El-Angbawi, Azad Aziz, William Shaw, Gunvor Semb, Gowri Vijay Reesu, Peter A. Mossey (Lead / Corresponding author)

Research output: Working paper/PreprintPreprint


Background: Previous studies embracing digital technology and automated methods of scoring dental arch relationships have shown that such technology is valid and accurate. To date, however there is no published literature on artificial intelligence and machine learning to completely automate the process of dental landmark recognition.

Objectives: This study aimed to develop and evaluate a fully automated system and software tool for the identification of landmarks on human teeth using geometric computing, image segmenting and machine learning technology.

Methods: 239 digital models were used in the automated landmark recognition (ALR) validation phase, 161 of which were digital models from cleft palate subjects aged 5 years. These were manually annotated to facilitate qualitative validation. Additionally, landmarks were placed on 20 adult digital models manually by three independent observers. The same models were subjected to scoring using the ALR software and the differences (in mm) were calculated. All the teeth from the 239 models were evaluated for correct recognition by the ALR with a breakdown to find which stages of the process caused the errors.
Results: The results revealed that 1526 out of 1915 teeth (79.7%) were correctly identified, and the accuracy validation gave 95% confidence intervals for the geometric mean error of [0.285, 0.317] for the humans and [0.269, 0.325] for ALR – a negligible difference.

Conclusions / Implications: It is anticipated that ALR software tool will have applications throughout clinical Dentistry and anthropology, and in research will constitute an accurate and objective tool for handling large datasets without the need for time intensive employment of experts to place landmarks manually.
Original languageEnglish
Place of PublicationCold Spring Harbor Laboratory
Publication statusPublished - 18 Dec 2020


  • 3D digital technology
  • automated landmark recognition
  • machine learning
  • scoring system


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