Use of the deep learning approach to measure alveolar bone level

Aim The goal was to use a deep convolutional neural network to measure the radiographic alveolar bone level to aid periodontal diagnosis. Materials and Methods A deep learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cemento-enamel junction) and image analysis to measure the radiographic bone level and assign radiographic bone loss (RBL) stages. The percentage of RBL was calculated to determine the stage of RBL for each tooth. A provisional periodontal diagnosis was assigned using the 2018 periodontitis classification. RBL percentage, staging, and presumptive diagnosis were compared with the measurements and diagnoses made by the independent examiners. Results The average Dice Similarity Coefficient (DSC) for segmentation was over 0.91. There was no significant difference in the RBL percentage measurements determined by DL and examiners (p=.65). The area under the receiver operating characteristics curve of RBL stage assignment for stages I, II, and III was 0.89, 0.90, and 0.90, respectively. The accuracy of the case diagnosis was 0.85. Conclusions The proposed DL model provides reliable RBL measurements and image-based periodontal diagnosis using periapical radiographic images. However, this model has to be further optimized and validated by a larger number of images to facilitate its application.

Automated summary

The study aimed to measure radiographic alveolar bone level using a deep convolutional neural network for periodontal diagnosis. A deep learning model integrating segmentation networks and image analysis was developed to provide reliable RBL measurements and image-based diagnosis. However, further optimization and validation with a larger number of images are needed for the model's application.