Deep learning-based dental plaque detection on permanent teeth and the influenced factors
10.3760/cma.j.cn112144-20201014-00526
- VernacularTitle:基于深度学习的恒牙牙菌斑智能判读系统的建立及其相关影响因素分析
- Author:
Wenzhe YOU
1
;
Aimin HAO
;
Shuai LI
;
Ziyi ZHANG
;
Ruozhu LI
;
Ruiqing SUN
;
Yong WANG
;
Bin XIA
Author Information
1. 北京大学口腔医学院·口腔医院儿童口腔科 国家口腔医学中心 国家口腔疾病临床医学研究中心 口腔数字化医疗技术和材料国家工程实验室 口腔数字医学北京市重点实验室 100081
- Keywords:
Dental plaque;
Artificial intelligence;
Deep learning;
Intraoral camera
- From:
Chinese Journal of Stomatology
2021;56(7):665-671
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an artificial intelligence system for detecting dental plaque on permanent teeth and find the influenced factors.Methods:Photos of the labial or buccal surfaces of the permanent teeth were taken by using an intraoral camera (1 280×960 pixels; TPC Ligang, Shenzhen, China) before and after applying the plaque-disclosing agent (Cimedical, Japan) in 25 volunteers [12 males, 13 femals, aged (23±3) years] recruided in accordance with the inclusion criteria from the students of Peking University School of Stomatology from October 2018 to June 2019. A total of 549 groups of photos were captured and then divided into a training dataset containing 440 groups of photos and a test dataset including 109 groups of photos. The scopes of teeth and dental plaque on photos were labeled using LabelMe (Windows 3.2.1, MIT, U S A). A DeepLab based deep learning system was designed for the intelligent detection of dental plaque on permanent teeth. The mean intersection over union (MIoU) was employed to indicate the detection accuracy. Matlab (Windows R2017a, MathWorks, U S A) was used to extract the plaque edge line of 109 groups of photos and to calculate the number of pixels for the measurement of the complexity of the plaque edge line. The percentage of dental plaque area was calculated. Multivariate linear regression was used to explore whether tooth site, plaque percentage, number of plaque edge line pixels and lens light spot location would influence the detection accuracy, of which P<0.05 was considered statistically significant. Results:The MIoU of the permanent tooth model was 0.700±0.191 when 440 photos were used for training and 109 photos were used for testing. In the regression model of significance test ( P<0.05), the percentage of plaque and the number of pixels on the edge of plaque had significant influence on the accuracy of dental plaque detection. The standardized coefficient of the number of pixels of the plaque edge line is -0.289, and the standardized coefficient of the percentage of plaque is -0.551. Conclusions:In the present study, an artificial intelligence system was built to detect dental plaque area on tooth photos collected by family intraoral camera. The system showed the ability to detect the dental plaque of permanent teeth. The more complex the marginal line of dental plaque and higher the percentage of dental plaque are, the lower the accuracy of plaque recognition is.
