  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.
