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Strut analysis for osteoporosis detection model using dental panoramic radiography

Authors
 Jae Joon Hwang  ;  Jeong-Hee Lee  ;  Sang-Sun Han  ;  Young Hyun Kim  ;  Ho-Gul Jeong  ;  Yoon Jeong Choi  ;  Wonse Park 
Citation
 Dentomaxillofacial Radiology, Vol.46(7) : 20170006, 2017 
Journal Title
 Dentomaxillofacial Radiology 
ISSN
 0250-832X 
Issue Date
2017
MeSH
Bone Density ; Decision Trees ; Female ; Fractals ; Humans ; Jaw/diagnostic imaging* ; Jaw/pathology* ; Male ; Middle Aged ; Osteoporosis/diagnostic imaging* ; Radiographic Image Interpretation, Computer-Assisted ; Radiography, Panoramic/methods* ; Sensitivity and Specificity ; Support Vector Machine
Keywords
computer-assisted ; fractals ; image processing ; mandible ; osteoporosis ; panoramic ; radiography
Abstract
OBJECTIVES: The aim of this study was to identify variables that can be used for osteoporosis detection using strut analysis, fractal dimension (FD) and the gray level co-occurrence matrix (GLCM) using multiple regions of interest and to develop an osteoporosis detection model based on panoramic radiography. METHODS: A total of 454 panoramic radiographs from oral examinations in our dental hospital from 2012 to 2015 were randomly selected, equally distributed among osteoporotic and non-osteoporotic patients (n = 227 in each group). The radiographs were classified by bone mineral density (T-score). After 3 marrow regions and the endosteal margin area were selected, strut features, FD and GLCM were analysed using a customized image processing program. Image upsampling was used to obtain the optimal binarization for calculating strut features and FD. The independent-samples t-test was used to assess statistical differences between the 2 groups. A decision tree and support vector machine were used to create and verify an osteoporosis detection model. RESULTS: The endosteal margin area showed statistically significant differences in FD, GLCM and strut variables between the osteoporotic and non-osteoporotic patients, whereas the medullary portions showed few distinguishing features. The sensitivity, specificity, and accuracy of the strut variables in the endosteal margin area were 97.1%, 95.7 and 96.25 using the decision tree and 97.2%, 97.1 and 96.9% using support vector machine, and these were the best results obtained among the 3 methods. Strut variables with FD and/or GLCM did not increase the diagnostic accuracy. CONCLUSION: The analysis of strut features in the endosteal margin area showed potential for the development of an osteoporosis detection model based on panoramic radiography.
Full Text
https://www.birpublications.org/doi/abs/10.1259/dmfr.20170006
DOI
10.1259/dmfr.20170006
Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Advanced General Dentistry (통합치의학과) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Radiology (영상치의학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers
Yonsei Authors
Park, Wonse(박원서) ORCID logo https://orcid.org/0000-0002-2081-1156
Jeong, Ho Gul(정호걸)
Choi, Yoon Jeong(최윤정) ORCID logo https://orcid.org/0000-0003-0781-8836
Han, Sang Sun(한상선) ORCID logo https://orcid.org/0000-0003-1775-7862
Hwang, Jae Joon(황재준)
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/160912
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