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. 2022 Mar;49(3):260-269.
doi: 10.1111/jcpe.13574. Epub 2021 Dec 31.

Use of the deep learning approach to measure alveolar bone level

Chun-Teh Lee  1 , Tanjida Kabir  2 , Jiman Nelson  1 , Sally Sheng  1 , Hsiu-Wan Meng  1 , Thomas E Van Dyke  3   4 , Muhammad F Walji  5 , Xiaoqian Jiang  2 , Shayan Shams  2   6
Affiliations

Use of the deep learning approach to measure alveolar bone level

Chun-Teh Lee et al. J Clin Periodontol. 2022 Mar.

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 ( 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.

Keywords: computer-assisted; deep learning; diagnosis; periodontal diseases; radiographic image interpretation.

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Conflict of interest statement

CONFLICT OF INTEREST

The authors declare that there is no conflict of interest for this article.

Figures

FIGURE 1
FIGURE 1
Flow diagram of the proposed computer-aided diagnosis (CAD) model. Segmentation models predicted the bone area, teeth, and cemento-enamel junction (CEJ) masks. Masks are processed to remove noises then overlaid to extract bone area, teeth, and CEJ line for radiographic bone loss (RBL) measurement and stage assignment for each tooth
FIGURE 2
FIGURE 2
Input image, ground truth (bone area, tooth, and cemento-enamel junction [CEJ] line), segmentation models’ output, and images after post-processing
FIGURE 3
FIGURE 3
Image analysis steps for calculating bone loss percentage for each tooth. Steps to calculate RBL percentage are as follows: (a) identify the intersecting points (yellow dots) between the tooth and bone area; (b) identify the intersecting points (green dots) between the tooth and CEJ line; (c) identify the mesial and distal root axes parallel to the tooth axis to locate the roots’ apexes (red dots); (d) calculate the distance between the CEJ and alveolar bone level at both mesial and distal sites (the line connecting the green dots and the yellow dots); (e) calculate the root length by identifying a line from each root apex to the CEJ line parallel to the tooth axis; and (f) divide the distance between the CEJ and alveolar bone level by the distance between the CEJ and root apex
FIGURE 4
FIGURE 4
Radiographic bone loss (RBL) percentage measurement distribution of the proposed computer-aided diagnosis (CAD) model and examiners. There was no significant difference in RBL percentage measurements between the CAD and examiners (p-value for all cases, stage I cases, stage II cases, and stage III cases = .65, .32, .27, .96). The bar inside the box represents the median. The upper end of the box represents the third quartile and the lower end of the box represents the first quartile. The ends of the whiskers represent maximum and minimum
See this image and copyright information in PMC

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