Knee Osteoarthritis (OA) is the most common form of arthritis, aecting millions of people around the world. The eects of the disease have been studied using the shape and texture features of bones in Posterior- Anterior (PA) and Lateral radiographs separately. In this work we compare the utility of features from each
view, and evaluate whether combining features from both is advantageous. We built a fully automated system to independently locate landmark points in both radiographic images using Random Forest Constrained Local Models. We extracted discriminative features from the two bony outlines using Appearance Models. The features were used to train Random Forest classiers to solve three specic tasks: (i) OA classication, distinguishing patients with structural signs of OA from the others; (ii) predicting future onset of the disease and (iii) predicting
which patients with no current pain will have a positive pain score later in a follow-up visit. Using a subset of the MOST dataset we show that the PA view has more discriminative features to classify and predict OA, while the lateral view contains features that achieve better performance in predicting pain, and that combining the features from both views gives a small improvement in accuracy of the classication compared to the individual views.