Light-Induced Fluorescence-Based Device and Hybrid Mobile App for Oral Hygiene Management at Home: Development and Usability Study

• Main Authors: Kim, Jun-Min, Lee, Woo Ram, Kim, Jun-Ho, Seo, Jong-Mo, Im, Changkyun
• Format: Article
• Language: English
• Published: JMIR Publications 2020-10-01
• Series: JMIR mHealth and uHealth
• Online Access: https://mhealth.jmir.org/2020/10/e17881

BackgroundDental diseases can be prevented through the management of dental plaques. Dental plaque can be identified using the light-induced fluorescence (LIF) technique that emits light at 405 nm. The LIF technique is more convenient than the commercial technique using a disclosing agent, but the result may vary for each individual as it still requires visual identification. ObjectiveThe objective of this study is to introduce and validate a deep learning–based oral hygiene monitoring system that makes it easy to identify dental plaques at home. MethodsWe developed a LIF-based system consisting of a device that can visually identify dental plaques and a mobile app that displays the location and area of dental plaques on oral images. The mobile app is programmed to automatically determine the location and distribution of dental plaques using a deep learning–based algorithm and present the results to the user as time series data. The mobile app is also built with convergence of naive and web applications so that the algorithm is executed on a cloud server to efficiently distribute computing resources. ResultsThe location and distribution of users’ dental plaques could be identified via the hand-held LIF device or mobile app. The color correction filter in the device was developed using a color mixing technique. The mobile app was built as a hybrid app combining the functionalities of a native application and a web application. Through the scrollable WebView on the mobile app, changes in the time series of dental plaque could be confirmed. The algorithm for dental plaque detection was implemented to run on Amazon Web Services for object detection by single shot multibox detector and instance segmentation by Mask region-based convolutional neural network. ConclusionsThis paper shows that the system can be used as a home oral care product for timely identification and management of dental plaques. In the future, it is expected that these products will significantly reduce the social costs associated with dental diseases.