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

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.

Automatic recognition of landmarks on digital dental models.

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).