Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments

Abstract

Background: The objectives 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 conventional 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 to train 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, tooth photos were acquired using a digital camera (3216×2136 pixels, Canon EOS 60D, Japan). One experienced pediatric dentist looked at these photos and marked the regions the photos containing dental plaque. After a week, the dentist drew the dental plaque area on the 98 photos taken by the digital camera a second time. In another round of comparison, 102 photos of primary teeth taken by the same intraoral camera to evaluate the diagnostic ability of each approach based on photos with lower-resolution (fewer pixels).The mean intersection-over-union (MIoU) metric was employed to indicate the detection accuracy.Results : The MIoU for the detection of dental plaque on the tested tooth photos was 0.726 ± 0.165.The MIoU of the dentist when diagnosing the 98 photos taken by the digital camera for the first time was 0.695 ± 0.269, while after one week, the MIoU of the dentist was 0.689 ± 0.253. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after a week. When both the dentist and the AI model assessed the 102 photos taken by the intraoral camera. the MIoU of the pediatric dentist was 0.652 ± 0.195, and the MIoU of the AI model was 0.724 ±0.159. The results of a paired t-test found no significant difference between the AI model and human specialist (P > .05) in diagnosing dental plaque on primary teeth.Conclusions : The AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist.The project was conceptualized by BX, AMH and YW. The project implementation was led by BX and 9 WZY. WZY wrote the first draft of the manuscript. BX and SL read and contributed to several versions of the manuscript. All the authors read and approved the final manuscript.

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