To assess whether application of a support vector machine learning algorithm to ancillary data obtained from posterior-anterior dual-energy X-ray absorptiometry (DEXA) studies could identify patients with lumbar spine (L1-L4) vertebral body fractures without additional DEXA imaging or radiation. Three hundred seven patients (199 without any fractures of the spine, and 108 patients with at least one fracture of the L1, L2, L3, or L4 vertebral bodies) who had DEXA studies were evaluated. Ancillary data from DEXA output was analyzed. The dataset was split into training (80%) and test (20%) datasets. Support vector machines (SVMs) with 10-fold cross-validation and different kernels were used to identify the best kernel based on the greatest area under the curve (AUC) and the best training vectors in the training dataset. The SVM with the best kernel was then applied to the test dataset to assess the accuracy of the SVM. Receiver operating characteristic (ROC) curves of the SVMs using different kernels in the test dataset were compared using DeLong's test. The SVM classifier with the linear kernel had the greatest AUC in the training dataset (AUC = 0.9258). The AUC of the SVM classifier with the linear kernel in the test dataset was 0.8963. The SVM classifier with the linear kernel had an overall average accuracy of 91.8% in the test dataset. The sensitivity, specificity, positive predictive value, and negative predictive of the SVM classifier with the linear kernel to detect lumbar spine fractures were 81.8%, 97.4%, 94.7%, and 90.5%, respectively. The SVM classifier with the linear kernel ROC curve had a significantly better AUC than the SVM classifier with the cubic polynomial kernel (P = 0.034) for discriminating between patients with lumbar spine fractures and control patients, but not significantly different from the SVM classifier with a radial basis function (RBF) kernel (P = 0.317) or the SVM classifier with a sigmoid kernel (P = 0.729). All fractures identified by the SVM classifiers were not prospectively identified by the radiologist. SVM analysis of ancillary data obtained from routine DEXA studies can identify lumbar spine fractures without the use of vertebral fracture assessment (VFA) DEXA imaging or radiation, and identify fractures missed by radiologists.

中文翻译：

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使用辅助向量机（SVM）分类器通过常规双能X射线吸收法（DEXA）研究通过计算机辅助检测腰椎意外骨折。

为了评估将支持向量机学习算法应用于从后-前双能X线骨密度仪（DEXA）研究获得的辅助数据是否可以识别腰椎（L1-L4）椎体骨折的患者，而无需进行其他DEXA成像或放射检查。评估了接受DEXA研究的307例患者（199例无脊柱骨折，108例具有至少L1，L2，L3或L4椎体骨折）。分析了来自DEXA输出的辅助数据。该数据集分为训练（80％）和测试（20％）数据集。使用具有10倍交叉验证和不同核的支持向量机（SVM），根据曲线下的最大面积（AUC）和训练数据集中的最佳训练向量来识别最佳核。然后将具有最佳内核的SVM应用于测试数据集，以评估SVM的准确性。使用DeLong的测试比较了使用测试数据集中不同内核的SVM的接收器工作特性（ROC）曲线。具有线性核的SVM分类器在训练数据集中具有最大的AUC（AUC = 0.9258）。测试数据集中具有线性核的SVM分类器的AUC为0.8963。具有线性核的SVM分类器在测试数据集中的总体平均准确度为91.8％。具有线性核的SVM分类器检测腰椎骨折的敏感性，特异性，阳性预测值和阴性预测分别为81.8％，97.4％，94.7％和90.5％。具有线性核ROC曲线的SVM分类器比具有三次多项式核的SVM分类器（P = 0.034）在区分腰椎骨折患者和对照患者方面具有更好的AUC，但与具有径向基函数（RBF）核（P = 0.317）或具有S形核的SVM分类器（P = 0.729）。SVM分类器识别出的所有骨折均未由放射科医生预先识别。从常规DEXA研究获得的辅助数据的SVM分析可以在不使用椎体骨折评估（VFA）DEXA成像或放射线的情况下识别腰椎骨折，并确定放射科医生遗漏的骨折。034）区分腰椎骨折患者和对照患者，但与具有径向基函数（RBF）核的SVM分类器（P = 0.317）或具有乙状结肠的SVM分类器（P = 0.729）没有显着差异。SVM分类器识别出的所有骨折均未由放射科医生预先识别。从常规DEXA研究获得的辅助数据的SVM分析可以在不使用椎体骨折评估（VFA）DEXA成像或放射线的情况下识别腰椎骨折，并确定放射科医生遗漏的骨折。034）区分腰椎骨折患者和对照患者，但与具有径向基函数（RBF）核的SVM分类器（P = 0.317）或具有乙状结肠的SVM分类器（P = 0.729）没有显着差异。SVM分类器识别出的所有骨折均未由放射科医生预先识别。从常规DEXA研究获得的辅助数据的SVM分析可以在不使用椎体骨折评估（VFA）DEXA成像或放射线的情况下识别腰椎骨折，并确定放射科医生遗漏的骨折。