Abstract
SummaryBackgroundPrevious 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.ObjectivesThis 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.MethodsTwo 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.ResultsThe 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/implicationsIt 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.
Highlights
Dentistry, like many other medical disciplines, is moving towards a more digital approach, using technology to the advantage of higher quality diagnostic and therapeutic patient care
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. 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
It is anticipated that ALR software tool will have applications throughout Dentistry and anthropology and in research will constitute an objective tool for handling large datasets without the need for time intensive employment of experts to place landmarks manually
Summary
Like many other medical disciplines, is moving towards a more digital approach, using technology to the advantage of higher quality diagnostic and therapeutic patient care. The challenge is to utilize the developments in digital imaging and computing technology to create software to automate and improve certain procedures such as diagnostics and outcome measurements. The sub-phenotypes are accompanied by complications from birth which include feeding and swallowing difficulties, speech impairment, hearing problems and dentofacial growth and development. The last of these, facial growth disturbance is an important outcome as it affects the quality of life has a significant psycho-social impact, especially during adolescence [1, 2] and it is important that it is measured accurately and objectively if possible
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