Predictive Characteristics of Rehabilitation Outcomes in Early Knee Osteoarthritis Using Multiple Deep Learning Models

Abstract

If rehabilitation outcomes in patients with early knee osteoarthritis could be predicted before treatment, it would provide valuable information for selecting appropriate candidates and therapeutic strategies. In this study, we predicted rehabilitation outcomes of early knee osteoarthritis from single x-ray image using multiple convolutional neural network (CNN) models and compared classification accuracy and highlighted features across models by visualizing the basis of prediction with Gradient-weighted Class Activation Mapping (Grad-CAM). From standing anteroposterior x-ray image of 64 patients, 128 cropped images centered on each knee joint were obtained, of which 70％ were used for training and 30％ for validation in a four-fold cross-validation. The training dataset was augmented to comprise 816 images in total. The CNN architecture evaluated were AlexNet, DenseNet-201, Inception-ResNet-v2, NASNet-Large, and ResNet-50. NASNet-Large achieved the highest accuracy of 62.5％, and Grad-CAM highlighted the patella and joint space as key regions associated with the highest classification performance.