Use of the Deep Learning Approach to Measure Alveolar Bone Level

TL;DR

This study developed a deep learning model to accurately measure alveolar bone loss from radiographs, aiding periodontal diagnosis with high reliability, but further validation is needed for clinical application.

Contribution

A novel deep convolutional neural network integrating segmentation and image analysis for automated alveolar bone level measurement and periodontal diagnosis.

Findings

Dice Similarity Coefficient over 0.91 for segmentation

No significant difference in RBL measurement between DL and examiners

Diagnosis accuracy of 85%

Abstract

Abstract: Aim: The goal was to use a Deep Convolutional Neural Network to measure the radiographic alveolar bone level to aid periodontal diagnosis. Material and methods: A Deep Learning (DL) model was developed by integrating three segmentation networks (bone area, tooth, cementoenamel 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 to 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 RBL percentage measurements determined by DL and examiners (p=0.65). The Area Under the Receiver Operating Characteristics Curve of RBL stage assignment for stage I, II and III was 0.89, 0.90 and 0.90, respectively. The accuracy of the case diagnosis was 0.85. Conclusion: 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.