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.

Objective:To develop a prototype artificial intelligence image recognition system for detecting dental caries, especially those without cavities, in children.Methods:Seven hundred and twelve intraoral photos, which were taken by dental professionals using a digital camera from October 2013 to June 2020 in the Department of Pediatric Dentistry, Peking University School and Hospital of Stomatology, were collected from the children who received dental treatment under general anesthesia. The well-documented post-treatment electronic dental record of each child was identified as label standard to determine whether the teeth were carious and the type of caries types such as caries that had become cavities (caries with cavities), pit and fissure caries that had not become cavities (pit and fissure caries) and proximal caries which the marginal ridge enamel had not been destroyed (proximal caries). The various teeth and caries types were labeled by pediatric dentists using VoTT software (Windows 2.1.0, Microsoft, U S A). There were five labeled groups: pit and fissure caries, approximal caries, non-carious approximal surfaces, caries with cavities and teeth without caries (including intact fillings). Each group was randomly divided into training dataset, validation dataset and test dataset at a ratio of 6.4∶1.6∶2.0 by using random number table. After using the labeled training dataset for deep learning training, a deep learning-based artificial intelligence (AI) image recognition system for detecting dental caries was established, with the caries probability greater than 50.0% as the criterion for determining caries. Sensitivity and accuracy were used as indicators of recognition specificity.Results:Seven hundred and twelve single-jaw intraoral photographs were segmented and annotated into 953 pit and fissure caries, 1 002 approximal caries, 3 008 caries with cavities, 3 189 teeth without caries and 862 non-carious approximal surfaces, totaly 9 014 labels. The sensitivities and specificities of the test set were 96.0% and 97.0% for caries with cavities, 95.8% and 99.0% for pit and fissure caries and 88.1% and 97.1% for approximal caries.Conclusions:The current AI system developed based on deep learning of the intra-oral photos in the present study showed the ability to detect dental caries. Furthermore, the AI system could accurately verify different types of dental caries such as caries with cavities, pit and fissure caries and proximal caries.

Objective:To compare the caries experience and the kinds of dental treatment between children with autism spectrum disorders (ASD) and children without systemic disease who were all treated under general anesthesia.Methods:Totally 103 children with ASD who received dental treatments under general anesthesia in 13 professional dental hospitals around China from April to November 2016 were included in the present study. A group of 97 children without systemic disease, according to the age, gender and application propensity score matching method, were chosen as controls, who received dental treatments under general anesthesia between January 2015 to November 2018 in the same hospitals as the children with ASD. Decay missing filling tooth (DMFT/dmft, DMFT for permanent teeth and dmft for primary teeth) indices of two groups of children and the contents of the dental treatments under general anesthesia were analyzed.Results:No significant difference of DMFT/dmft index ［ M( Q25, Q75)］ was found between children with ASD group ［0 (0, 3)/11(8, 14)］ and control group ［0 (0, 3)/9(7, 13)］ ( P>0.05). The average number of dental treatments under general anesthesia and the average number of endodontic treatment in children with ASD were 13 (11, 15) and 3 (2, 6) teeth respectively, while those in the control group were 12 (9, 14) and 2 (1, 4) teeth respectively, the differences were statistically significant ( P<0.01, P<0.05). Conclusions:No significant difference was found between children with ASD and the normal controls who receive dental treatments under general anesthesia in DMFT/dmft index, but the treatment needs of children with ASD is relatively higher, and their tooth decay is relatively severer.