Suppr超能文献

基于光致荧光的家用口腔卫生管理设备和混合移动应用程序:开发和可用性研究。

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

机构信息

Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.

Department of Electronics and Information Engineering, Hansung University, Seoul, Republic of Korea.

出版信息

JMIR Mhealth Uhealth. 2020 Oct 16;8(10):e17881. doi: 10.2196/17881.

Abstract

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.

摘要

背景

通过对牙菌斑的管理可以预防牙科疾病。牙菌斑可以使用发出 405nm 光的光致荧光(LIF)技术来识别。LIF 技术比使用显色剂的商业技术更方便,但由于仍需要进行目视识别,因此结果可能因个体而异。

目的

本研究旨在介绍并验证一种基于深度学习的家庭口腔卫生监测系统,使在家中识别牙菌斑变得更加容易。

方法

我们开发了一种基于 LIF 的系统,该系统由一个可以直观识别牙菌斑的设备和一个可在口腔图像上显示牙菌斑位置和面积的移动应用程序组成。该移动应用程序使用基于深度学习的算法自动确定牙菌斑的位置和分布,并将结果作为时间序列数据呈现给用户。该移动应用程序还采用了傻瓜应用程序和网络应用程序的融合构建,以便算法在云服务器上执行,从而有效地分配计算资源。

结果

可以通过手持式 LIF 设备或移动应用程序识别用户牙菌斑的位置和分布。设备中的颜色校正滤镜是使用颜色混合技术开发的。移动应用程序被构建为一个混合应用程序,结合了原生应用程序和网络应用程序的功能。通过移动应用程序上可滚动的 WebView,可以确认牙菌斑时间序列的变化。用于牙菌斑检测的算法已实现,可在 Amazon Web Services 上运行,用于单步多框检测器的目标检测和基于掩模区域的卷积神经网络的实例分割。

结论

本文表明该系统可用作家庭口腔护理产品,及时识别和管理牙菌斑。未来,这些产品有望显著降低与牙科疾病相关的社会成本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8d14/7600004/467ff50447a2/mhealth_v8i10e17881_fig1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验