Use of the Deep Learning Approach to Measure Alveolar Bone Level

Authors

Chun-Teh Lee
Tanjida Kabir
Jiman Nelson
Sally Sheng
Hsiu-Wan Meng
Thomas E Van Dyke
Muhammad F Walji
Xiaoqian Jiang
Shayan Shams

Language

English

Publication Date

3-1-2022

Journal

Journal of Clinical Periodontology

DOI

10.1111/jcpe.13574

PMID

34879437

PMCID

PMC9026777

PubMedCentral® Posted Date

4-22-2022

PubMedCentral® Full Text Version

Author MSS

Abstract

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 (

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.

Keywords

Deep Learning, Humans, Image Processing, Computer-Assisted, Neural Networks, Computer, Periodontitis, diagnosis, deep learning, periodontal diseases, radiographic image interpretation

Published Open-Access

yes

Recommended Citation

Lee, Chun-Teh; Kabir, Tanjida; Nelson, Jiman; et al., "Use of the Deep Learning Approach to Measure Alveolar Bone Level" (2022). Faculty, Staff and Student Publications. 491.
https://digitalcommons.library.tmc.edu/uthshis_docs/491