BACKGROUND: Early diagnosis of @entity170 can potentially decrease the risk of @entity174 and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal @entity1 with low @entity79 ( @entity79 ) or @entity170 . 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 @entity1 with @entity79 . METHODS: We employed our newly adopted SVM method for continuous measurement of the cortical width of the mandible on dental panoramic radiographs to identify @entity1 with @entity79 or @entity170 . 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 @entity1 with @entity79 ( @entity79 T-score of -1.0 or less) at the lumbar spine and femoral neck in 100 postmenopausal @entity1 (>= 50 years old) with no previous diagnosis of @entity170 . Sixty @entity1 were used for system training, and 40 were used in testing. RESULTS: The sensitivity and specificity using RBF kernel-SVM method for identifying @entity1 with @entity79 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 @entity1 with @entity79 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 @entity1 with @entity2331 .

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

@entity1 :: ('9606', 'Species') :: ['women'] @entity174 :: ('MESH:D050723', 'Disease') :: ['fractures'] @entity170 :: ('MESH:D010024', 'Disease') :: ['osteoporosis'] @entity79 :: ('MESH:D001847', 'Disease') :: ['bone mineral density', 'low BMD', 'BMD'] @entity2331 :: ('MESH:D001851', 'Disease') :: ['low skeletal BMD']
@entity170 :: (MESH:D010024,Disease) :: ['osteoporosis']
