  Knee osteoarthritis (OA) is very common progressive and degenerative
musculoskeletal disease worldwide creates a heavy burden on patients with
reduced quality of life and also on society due to financial impact. Therefore,
any attempt to reduce the burden of the disease could help both patients and
society. In this study, we propose a fully automated novel method, based on
combination of joint shape and convolutional neural network (CNN) based bone
texture features, to distinguish between the knee radiographs with and without
radiographic osteoarthritis. Moreover, we report the first attempt at
describing the bone texture using CNN. Knee radiographs from Osteoarthritis
Initiative (OAI) and Multicenter Osteoarthritis (MOST) studies were used in the
experiments. Our models were trained on 8953 knee radiographs from OAI and
evaluated on 3445 knee radiographs from MOST. Our results demonstrate that
fusing the proposed shape and texture parameters achieves the state-of-the art
performance in radiographic OA detection yielding area under the ROC curve
(AUC) of 95.21%
