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Use of the deep learning approach to measure alveolar bone level
Author(s) -
Lee ChunTeh,
Kabir Tanjida,
Nelson Jiman,
Sheng Sally,
Meng HsiuWan,
Van Dyke Thomas E.,
Walji Muhammad F.,
Jiang Xiaoqian,
Shams Shayan
Publication year - 2022
Publication title -
journal of clinical periodontology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.456
H-Index - 151
eISSN - 1600-051X
pISSN - 0303-6979
DOI - 10.1111/jcpe.13574
Subject(s) - dental alveolus , radiography , periodontitis , medicine , dentistry , convolutional neural network , receiver operating characteristic , medical diagnosis , segmentation , orthodontics , artificial intelligence , radiology , computer science
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.