Jae Joon Hwang1, Jeong-Hee Lee1, Sang-Sun Han1, Young Hyun Kim1, Ho-Gul Jeong1, Yoon Jeong Choi2, Wonse Park3. 1. 1 Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea. 2. 2 Department of Orthodontics, Yonsei University College of Dentistry, Seoul, Republic of Korea. 3. 3 Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul, Republic of Korea.
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.
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-osteoporoticpatients (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-osteoporoticpatients, 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.
Authors: Roberto Civitelli; Thomas K Pilgram; Mary Dotson; Jane Muckerman; Nancy Lewandowski; Reina Armamento-Villareal; Naoko Yokoyama-Crothers; E Eugenia Kardaris; Jay Hauser; Sheldon Cohen; Charles F Hildebolt Journal: Arch Intern Med Date: 2002-06-24
Authors: Stuart C White; Akira Taguchi; David Kao; Sam Wu; Susan K Service; Douglas Yoon; Yoshikazu Suei; Takashi Nakamoto; Keiji Tanimoto Journal: Osteoporos Int Date: 2004-07-27 Impact factor: 4.507
Authors: D de Sá Cavalcante; M G da Silva Castro; A R P Quidute; M R A Martins; A M P L Cid; P G de Barros Silva; J Cadwell Williams; F S Neves; T R Ribeiro; F W G Costa Journal: Osteoporos Int Date: 2019-08-02 Impact factor: 4.507