Strut analysis for osteoporosis detection model using dental panoramic radiography

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.

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.

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.

The analysis of strut features in the endosteal margin area showed potential for the development of an osteoporosis detection model based on panoramic radiography.