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Open Access Highly Accessed Research article

Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system

M S Kavitha1, Akira Asano12, Akira Taguchi3*, Takio Kurita1 and Mitsuhiro Sanada4

Author Affiliations

1 Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima 739-8527, Japan

2 Faculty of Informatics, Kansai University, 2-1-1 Ryozenji-cho, Takatsuki, Osaka 506-1095, Japan

3 Department of Oral and Maxillofacial Radiology, Matsumoto Dental University, 1780 Hirooka-Gohara, Shiojiri, Nagano 399-0781, Japan

4 Sanada Hospital, Minamimachi 3-13-21, Minami-ku, Hiroshima 734-0007, Japan

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BMC Medical Imaging 2012, 12:1  doi:10.1186/1471-2342-12-1

Published: 16 January 2012

Abstract

Background

Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD.

Methods

We employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify women with low BMD or osteoporosis. The original X-ray image was enhanced, cortical boundaries were determined, distances among the upper and lower boundaries were evaluated and discrimination was performed by a radial basis function. We evaluated the diagnostic efficacy of this newly developed method for identifying women with low BMD (BMD T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal women (≥50 years old) with no previous diagnosis of osteoporosis. Sixty women were used for system training, and 40 were used in testing.

Results

The sensitivity and specificity using RBF kernel-SVM method for identifying women with low BMD were 90.9% [95% confidence interval (CI), 85.3-96.5] and 83.8% (95% CI, 76.6-91.0), respectively at the lumbar spine and 90.0% (95% CI, 84.1-95.9) and 69.1% (95% CI, 60.1-78.6), respectively at the femoral neck. The sensitivity and specificity for identifying women with low BMD at either the lumbar spine or femoral neck were 90.6% (95% CI, 92.0-100) and 80.9% (95% CI, 71.0-86.9), respectively.

Conclusion

Our results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.