Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system.
Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system.
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DOI：
10.1186/1471-2342-12-1
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发表时间：
2012-01-16
影响因子：
2.7
通讯作者：
Sanada M
中科院分区：
文献类型：
--
作者：
Kavitha MS;Asano A;Taguchi A;Kurita T;Sanada M
早期诊断骨质疏松症可能会降低骨折的风险，提高生活质量。在牙科全景片上检测下颌骨薄层下部皮质有助于鉴别绝经后低骨密度(BMD)或骨质疏松妇女。我们研究的目的是评估使用基于核的支持向量机(支持向量机)学习牙科全景片上的下颌骨皮质宽度来识别低骨密度的绝经后妇女的诊断效果。我们使用我们新采用的支持向量机方法在牙科全景X线片上连续测量下颌骨皮质宽度，以识别低骨密度或骨质疏松的女性。对原始X线图像进行增强，确定皮质边界，计算上下边界之间的距离，并用径向基函数进行判别。我们评估了这一新开发的方法在100名绝经后妇女(≥，50岁)中的诊断效果，该方法用于识别腰椎和股骨颈骨密度低(骨密度T评分为-1.0或更低)的妇女，此前没有骨质疏松症的诊断。60名妇女被用于系统培训，40名妇女被用于测试。采用径向基核-支持向量机对女性低骨密度的诊断敏感性和特异性在腰椎分别为90.9%[95%可信区间，85.3~96.5]和83.8%(95%可信区间，76.6~91.0)，在股骨颈分别为90.0%(95%可信区间，84.1~95.9)和69.1%(95%可信区间，60.1~78.6)。诊断腰椎或股骨颈低骨密度的敏感性和特异性分别为90.6%(95%CI，92.0-100)和80.9%(95%CI，71.0-86.9)。我们的结果表明，新开发的支持向量机方法系统将有助于识别低骨密度的绝经后妇女。
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. 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. 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. Our results suggest that the newly developed system with the SVM method would be useful for identifying postmenopausal women with low skeletal BMD.