Randomised controlled trial on differential learning of toothbrushing in 6- to 9-year-old children

et al. 2020

BMC Oral Health

107

Background: Dental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, plaque detection and control are extremely important for children's oral health. The objectives of this study were to design a deep learning-based artificial intelligence (AI) model to detect plaque on primary teeth and to evaluate the diagnostic accuracy of the model. Methods: A conventional neural network (CNN) framework was adopted, and 886 intraoral photos of primary teeth were used for training. To validate clinical feasibility, 98 intraoral photos of primary teeth were assessed by the AI model. Additionally, tooth photos were acquired using a digital camera. One experienced pediatric dentist examined the photos and marked the regions containing plaque. Then, a plaque-disclosing agent was applied, and the areas with plaque were identified. After 1 week, the dentist drew the plaque area on the 98 photos taken by the digital camera again to evaluate the consistency of manual diagnosis. Additionally, 102 intraoral photos of primary teeth were marked to denote the plaque areas obtained by the AI model and the dentist to evaluate the diagnostic capacity of each approach based on lower-resolution photos. The mean intersection-over-union (MIoU) metric was employed to indicate detection accuracy. Results: The MIoU for detecting plaque on the tested tooth photos was 0.726 ± 0.165. The dentist's MIoU was 0.695 ± 0.269 when first diagnosing the 98 photos taken by the digital camera and 0.689 ± 0.253 after 1 week. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after 1 week. When the dentist and the AI model assessed the 102 intraoral photos, the MIoU was 0.652 ± 0.195 for the dentist and 0.724 ± 0.159 for the model. 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. This finding illustrates the potential of such AI technology to help improve pediatric oral health.

Section: Discussionmentioning

confidence: 99%

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

You¹,

et al. 2020

BMC Oral Health

107

“…The majority of the in vivo measurement techniques are based upon subjective assessments by trained experts of the amount of plaque on teeth [13,14,15]. Two common indices used to assess plaque levels are the Turesky-modified Quigley-Hein plaque index (T-QHI) and the Silness-Löe plaque index score [16,17].…”

Section: Discussionmentioning

confidence: 99%

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

You

et al. 2020

Preprint

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.

“…Until now, no research has been conducted in the field of dentistry regarding the use of AI to detect dental plaque. The majority of the in vivo measurement techniques are based upon subjective assessments by trained experts of the amount of plaque on teeth [13,14,15]. Two common indices used to assess plaque levels are the Turesky-modified Quigley-Hein plaque index (T-QHI) and the Silness-Löe plaque index score [16,17].…”

Section: Discussionmentioning

confidence: 99%

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

You

et al. 2020

Preprint

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 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. Then, a plaquedisclosing agent was applied, and the areas of dental plaque were identified. After a week, the dentist drew the dental plaque area on the 98 photos taken by the digital camera a second time to evaluate the consistency of manual diagnosis. In another round of comparison, 102 photos of primary teeth taken by the same intraoral camera were marked to denote the dental plaque area obtained both by the AI model and by the pediatric dentist to evaluate the diagnostic ability of each approach based on photos with lower-resolution (fewer pixels) than those taken by the digital camera. 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. 3Conclusions: The AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist. This finding illustrates the potential application in adopting such AI technology for helping children improve their oral health.