[Deep learning-based dental plaque detection on permanent teeth and the influenced factors].

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.

[Twoyear outcomes and the influence factors of indirect pulp treatment in primary teeth: a retrospective study].

OBJECTIVE: To evaluate the outcomes and to find out the influence factors of indirect pulp treatment in primary teeth. METHODS: Children who received indirect pulp treatment in primary teeth in the Department of Pediatric Dentistry in Peking University School and Hospital of Stomatology from August, 2014 to September, 2016 were collected in the Electronic Medical Record Database of Peking University School and Hospital of Stomatology, via the database for scientific research provided by the corporation of Kaientai. The children selected as the subjects of this study were followed up over 1.5 years, and they were under 9 years old if in the group of primary molars while the children in the group of primary anterior teeth were under 4 and a half years old. Those children who were not reviewed regularly or didn't have complete medical records were removed. Basic information, the relevant medical records and radiographic records of those children were collected. All teeth were examined clinically and classified into 2 outcomes, teeth in group H were regarded as succeeded, and teeth in group P were regarded as failed. Survival analysis was applied. The survival rate and survival time of the deciduous teeth calculated. Multivariate analysis was performed by using Cox proportional hazard model. RESULTS: One hundred and six children were finally included, aged from 1.6 to 8.8 years, with the mean age of (5.0±1.7) years. 168 primary teeth (122 primary molars, 46 primary anterior teeth) were included, and the average follow up time was (729±244) days. Thirty-five primary teeth (23 primary molars, 12 primary anterior teeth) failed upon clinical or radiographic examinations by September, 2018. The cumulative survival probability of half a year, one year, one year and a half, two years, two and a half years for the indirect pulp treatment was 93.5%, 92.9%, 87.5%, 82.7%, and 75.5% through the KaplanMeier method, respectively. Through the analysis of Cox proportional hazard model, in primary molars, the survival probability tended to be lower when the number of tooth surface affected by caries was greater (OR=1.709, P<0.05). Compared with primary molars, the survival probability of primary anterior teeth was lower, but the difference was not significant. CONCLUSION: Complying the current instructions in our department, the cumulative survival probability of two and a half years after the indirect pulp treatment in primary teeth was 75.5%. In primary molars, the survival probability tended to be lower when the number of tooth surfaces affected by caries increased.

[A long-term follow-up study with endoscope in chronic gastritis].

A 10 to 17 years endoscope follow-up was performed to 138 cases of chronic gastritis. The result showed that 118 cases still proved to be chronic gastritis, and the increase of chronic atrophic gastritis (CAG) in numbers. 15 cases developed into peptic ulcer. 5 cases to carcinoma (4 cases were early carcinoma). The time of cancerization differed from 2 to 12 years. The rate of cancerization of CAG reached 7.46%. The rate of cancerization of intestinal metaplasia (IM) were 8.20%. 13 cases of IM were mucus histo-chemical stained, and five of them contained sulfuric acid mucus, one of the 5 cases cancerized. 3 of 14 cases with atypical hyperplasia (ATP) turned into stomach cancer. We believe that chronic gastritis, especially CAG with ATP and IM, or with sulfuric acid mucus of IM had a high possibility of cancerization with the increase of age, and should be followed up for a long time.