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. 2022 Jan 25;44(1):43-50.
doi: 10.1093/ejo/cjab012.

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

Brénainn Woodsend  1 , Eirini Koufoudaki  2 , Ping Lin  1 , Grant McIntyre  2 , Ahmed El-Angbawi  3 , Azad Aziz  3 , William Shaw  3 , Gunvor Semb  3 , Gowri Vijay Reesu  3 , Peter A Mossey  2
Affiliations

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

Brénainn Woodsend et al. Eur J Orthod. .

Abstract

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: Two hundred and thirty-nine 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 3 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.

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Figures

Figure 1.
Figure 1.
The modified Huddart–Bodenham (MHB) scoring system for CLP outcome measurement. CLP, clefts of the lip and palate.
Figure 2.
Figure 2.
The stepwise process used for automated landmark recognition (ALR) in the deciduous dentition.
Figure 3.
Figure 3.
Peak points found (black arrows) and height threshold (blue) on dental model.
Figure 4.
Figure 4.
A few different tooth partition scenarios demonstrating peaks (balls) and their areas on a dental model of a permanent dentition.
Figure 5.
Figure 5.
Surface area tooth characteristic chart showing surface areas from the training set by tooth type during tooth assignment.
Figure 6.
Figure 6.
The distribution of placement errors (bar graphs) from the humans’ and ALR’s landmarks and best-fit Gaussian distributions (lines) for each. ALR, automated landmark recognition.
See this image and copyright information in PMC

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