Intelligent analysis of coronal alignment in lower limbs based on radiographic image with convolutional neural network

Abstract

One of the first tasks in osteotomy and arthroplasty is to identify the lower limb yams 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 degrees in 82.3% of the cases.