Automatic dental biofilm detection based on deep learning

Aim To estimate the automated biofilm detection capacity of the U‐Net neural network on tooth images. Materials and Methods Two datasets of intra‐oral photographs taken in the frontal and lateral views of permanent and deciduous dentitions were employed. The first dataset consisted of 96 photographs taken before and after applying a disclosing agent and was used to validate the domain's expert biofilm annotation (intra‐class correlation coefficient = .93). The second dataset comprised 480 photos, with or without orthodontic appliances, and without disclosing agents, and was used to train the neural network to segment the biofilm. Dental biofilm labelled by the dentist (without disclosing agents) was considered the ground truth. Segmentation performance was measured using accuracy, F1 score, sensitivity, and specificity. Results The U‐Net model achieved an accuracy of 91.8%, F1 score of 60.6%, specificity of 94.4%, and sensitivity of 67.2%. The accuracy was higher in the presence of orthodontic appliances (92.6%). Conclusions Visually segmenting dental biofilm employing a U‐Net is feasible and can assist professionals and patients in identifying dental biofilm, thus improving oral hygiene and health.