Background: Dental plaque is the cause of many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, the detection and control of dental plaque are of great significance to children’s oral health. The objectives of this study are to design an artificial intelligence (AI) model based on deep learning to detect dental plaque on primary teeth and evaluate the diagnostic accuracy of the AI model.
Methods : A convolutional neural network (CNN) framework was adopted, and a total of 886 photos of primary teeth taken by an intraoral camera (1280*960 pixels; TPC Ligang, Shenzhen, China) were used for training the AI model. To validate the clinical feasibility, 98 photos of primary teeth taken by the intraoral camera were assessed by the AI model. Additionally, teeth photos were taken with a digital camera (3216*2136 pixels, Canon EOS 60D, Japan). One experienced pediatric dentist looked at these photos and drew the region of dental plaque on them. Then, a plaque-disclosing agent was applied, and the areas of dental plaque were identified. The mean intersection-over-union (MIoU) was employed to indicate the detection accuracy.
Results : Compared to that of the dentist, the AI model demonstrated a higher MIoU (0.6947 vs 0.7364). However, the 2 modalities yielded no significant difference in diagnosing dental plaque on primary teeth (P > .05).
Conclusions : The AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with the experienced pediatric dentist. This finding illustrates the potential application in adopting such AI technology in helping children improve their oral health.