Dataset Information

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

ABSTRACT: 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.

SUBMITTER: You W

PROVIDER: S-EPMC7222297 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

You Wenzhe W Hao Aimin A Li Shuai S Wang Yong Y Xia Bin B

BMC oral health 20200513 1

<h4>Background</h4>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.<h4>Methods</h4>A conventional neural network (CNN) framework was adopted, and 886 intraoral photos of prim ...[more]

PMID: 32404094

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Project description:ObjectivesThis study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images.MethodsThe CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification.ResultsThe diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations.ConclusionsThis research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry.Clinical significanceThis study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.

Dataset Information

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

Publications

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

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