Ideally, X-ray imaging of artificial knee joints should perfectly match the joint surfaces. However, due to factors such as individual differences between patients and the skill of technicians, it is difficult to achieve perfect matching in one shot. In addition, the acceptance criteria of images are often left to the judgment of individuals, and unnecessary retakes often occur. In order to solve the problems caused by the increase in the number of retakes, we develop an automated assessment system for alignment of artificial knee joint in X-ray images using Convolutional Neural Network (CNN), which has excellent ability in image recognition. In this study, we first performed a preliminary study using an artificial knee phantom, and then examined the usefulness of this method in detail using clinical images. In the former, we used VGG16 as a CNN model for image classification and evaluated its classification performance. In the latter, we used clinical images of 461 cases for which the acceptance or rejection of imaging had been confirmed. We used several CNN models for image classification and compared their performance. In both examinations the overall classification rate exceeded 80%, and VGG16 had the highest classification performance among the CNN models. These results suggest the possibility that this method can reduce unnecessary retakes.
