One of the first tasks in osteotomy and arthroplasty is to identify the lower limb varus and valgus deformity status. The measurement of a set of angles to determine this status is generally performed manually with the measurement accuracy depending heavily on the experience of the person performing the measurements. This study proposes a method for calculating the required angles in lower limb radiographic (X-ray) images supported by the convolutional neural network. To achieved high accuracy in the measuring process, not only is a decentralized deep learning algorithm, including two orders for the radiographic, utilized, but also a training dataset is built based on the geometric knowledge related to the deformity correction principles. The developed algorithm performance is compared with standard references consisting of manually measured values provided by doctors in 80 radiographic images exhibiting an impressively low deviation of less than 1.5° in 82.3% of the cases.

中文翻译：

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基于卷积神经网络的射线图像对下肢冠状位对准的智能分析。

截骨术和关节置换术的首要任务之一是确定下肢内翻和外翻畸形状态。确定该状态的一组角度的测量通常以测量精度手动进行，这在很大程度上取决于进行测量的人员的经验。这项研究提出了一种方法，用于计算卷积神经网络支持的下肢放射线照相（X射线）图像中的所需角度。为了在测量过程中实现高精度，不仅利用了分散的深度学习算法（包括两个射线照相的顺序），而且还基于与畸形校正原理相关的几何知识构建了训练数据集。