Towards a Fully Automated Diagnostic System for Orthodontic Treatment in Dentistry

Abstract

A deep learning technique has emerged as a successful approach for diagnostic imaging. Along with the increasing demands for dental healthcare, the automation of diagnostic imaging is increasingly desired in the field of orthodontics for many reasons (e.g., remote assessment, cost reduction, etc.). However, orthodontic diagnoses generally require dental and medical scientists to diagnose a patient from a comprehensive perspective, by looking at the mouth and face from different angles and assessing various features. This assessment process takes a great deal of time even for a single patient, and tends to generate variation in the diagnosis among dental and medical scientists. In this paper, the authors propose a deep learning model to automate diagnostic imaging, which provides an objective morphological assessment of facial features for orthodontic treatment. The automated diagnostic imaging system dramatically reduces the time needed for the assessment process. It also helps provide objective diagnosis that is important for dental and medical scientists as well as their patients because the diagnosis directly affects to the treatment plan, treatment priorities, and even insurance coverage. The proposed deep learning model outperforms a conventional convolutional neural network model in its assessment accuracy. Additionally, the authors present a work-in-progress development of a data science platform with a secure data staging mechanism, which supports computation for training our proposed deep learning model. The platform is expected to allow users (e.g., dental and medical scientists) to securely share data and flexibly conduct their data analytics by running advanced machine learning algorithms (e.g., deep learning) on high performance computing resources (e.g., a GPU cluster).