PURPOSE The aim of this work is to propose a classification algorithm to automatically detect treatment for scoliosis (brace, implant or no treatment) in postero-anterior radiographs. Such automatic labelling of radiographs could represent a step towards global automatic radiological analysis. METHODS Seven hundred and ninety-six frontal radiographies of adolescents were collected (84 patients wearing a brace, 325 with a spinal implant and 387 reference images with no treatment). The dataset was augmented to a total of 2096 images. A classification model was built, composed by a forward convolutional neural network (CNN) followed by a discriminant analysis; the output was a probability for a given image to contain a brace, a spinal implant or none. The model was validated with a stratified tenfold cross-validation procedure. Performance was estimated by calculating the average accuracy. RESULTS 98.3% of the radiographs were correctly classified as either reference, brace or implant, excluding 2.0% unclassified images. 99.7% of brace radiographs were correctly detected, while most of the errors occurred in the reference group (i.e. 2.1% of reference images were wrongly classified). CONCLUSION The proposed classification model, the originality of which is the coupling of a CNN with discriminant analysis, can be used to automatically label radiographs for the presence of scoliosis treatment. This information is usually missing from DICOM metadata, so such method could facilitate the use of large databases. Furthermore, the same model architecture could potentially be applied for other radiograph classifications, such as sex and presence of scoliotic deformity.

中文翻译：

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卷积神经网络，用于在射线照片中检测脊柱侧弯的治疗。

目的这项工作的目的是提出一种分类算法，以自动检测后前X线片中脊柱侧凸的治疗方法（支架，植入物或不治疗）。放射线照片的这种自动标记可以代表朝着全局自动放射学分析迈出的一步。方法收集了796例青少年的额骨影像学影像（84例带有支架的患者，325例带脊柱植入物的患者和387例未经治疗的参考图像）。数据集增加到总共2096张图像。建立了一个分类模型，该模型由正向卷积神经网络（CNN）和判别分析组成；输出是给定图像包含支架，脊柱植入物或不包含的可能性。通过分层的十倍交叉验证程序对模型进行验证。通过计算平均准确度来评估性能。结果98.3％的X射线照片正确分类为参考，支架或植入物，不包括2.0％的未分类图像。正确检测出99.7％的支架X光片，而大多数错误发生在参考组中（即2.1％的参考图像被错误分类）。结论所提出的分类模型（其独创性是CNN与判别分析的结合）可用于自动标记X线照片以显示是否存在脊柱侧弯。通常，DICOM元数据中缺少此信息，因此这种方法可以促进大型数据库的使用。此外，相同的模型架构可能会应用于其他X射线照片分类，例如性别和脊柱侧凸畸形的存在。