TY  - JOUR
AU  - Kim, Jun-Min
AU  - Lee, Woo Ram
AU  - Kim, Jun-Ho
AU  - Seo, Jong-Mo
AU  - Im, Changkyun
PY  - 2020
DA  - 2020/10/16
TI  - Light-Induced Fluorescence-Based Device and Hybrid Mobile App for Oral Hygiene Management at Home: Development and Usability Study
JO  - JMIR Mhealth Uhealth
SP  - e17881
VL  - 8
IS  - 10
KW  - dental plaque
KW  - oral hygiene
KW  - red fluorescence
KW  - mobile health
KW  - deep learning
KW  - object detection
KW  - instance segmentation
AB  - Background: Dental 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. Objective: The 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. Methods: We 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. Results: The 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. Conclusions: This 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. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2020/10/e17881
UR  - https://doi.org/10.2196/17881
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33064097
DO  - 10.2196/17881
ID  - info:doi/10.2196/17881
ER  -