BackgroundAn accurate and valid caries prevention policy is absent in Zhejiang because of insufficient data. Therefore, the aim of this study was to investigate oral health status and related risk factors in 12- to 14-year-old students in Zhejiang, China.Material/MethodsUsing multi-stage, stratified, random sampling, we recruited a total of 4860 students aged 12 to 14 years old from 6 regions in Zhejiang in this cross-sectional study. Dental caries was measured using the Decayed, Missing and Filled Teeth (DMFT) index and the Significant Caries Index (SiC). Information concerning family background and relevant behaviors was collected in a structured questionnaire. Logistic regression analysis was used to study risk factors related to dental caries.ResultsThe overall prevalence of dental caries was 44% and the mean DMFT and SiC scores were 1.14 and 3.11, respectively. Female students had a higher level of dental caries than male students (P<0.01). The annual increase in caries prevalence was 3% with increasing age, and the DMFT score was 0.15. The results of logistic regression analysis showed that female sex, older age, snacks consumption once or more per day, fair or poor self-assessment of dental health, toothache experience, and dental visits were the most significant risk factors for dental caries, with odds ratios ranging from 1.24 to 2.25 (P<0.01).ConclusionsThe prevalence of dental caries in 12- to 14-year-old students in Zhejiang was low, with a tendency to increase compared with previous oral surveys. Female sex, older age, increased sugar intake, poor oral health self-assessment, and bad dental experience were the most important factors increasing dental caries risks.

Objectives: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. Methods: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. Results: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. Conclusion: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.

Dental caries is one of the most common infectious diseases affecting 6–8-year-old children, especially their first permanent molars (FPMs). This study explored the prevalence of dental caries on FPMs by analyzing the oral health status of 1,423,720 children aged 6–8 years in Zhejiang Province, China. The data were extracted from the dental electronic records of the schoolchildren attending the Oral Health Promotion Project (OHPP), conducted during 2013–2017 in Zhejiang Province. Multiple logistic regression models were used to determine the factors affecting dental caries. Boys and girls accounted for 53.2% and 46.8% of the subjects, respectively. From 2013 to 2017, the prevalence of dental caries on FPMs increased: 2013: 20.4%; 2014: 25.3%; 2015: 24.5%; 2016: 27.0%; and 2017: 29.0%, despite the OHPP conducted. Based on multiple logistic regression model, girls had a significantly higher risk of FPM caries compared to boys (OR = 1.38, 95% CI: 1.37–1.39, p < 0.0001); compared with the caries rates in urban areas, the caries risk was significantly higher in rural areas (OR = 1.15, 95% CI: 1.14–1.16, p < 0.0001). In terms of geographic location in Zhejiang Province, the odds ratios of the caries risk of the east, south, west, and north were 1.35 (1.33–1.36), 1.3 (1.28–1.31), 0.81 (0.8–0.83), and 0.82 (0.81–0.84), respectively (p < 0.0001), by considering the central region as a reference. The caries prevalence of FPMs was high, with an increasing tendency and gender, social, cultural, and environmental factors affecting the caries prevalence.