Knee Osteoarthritis Detection Using a Machine Learning Method from Magnetic Resonance Imaging: A 3-D Independent Component Analysis-Based Approach

In this chapter, a machine learning-based knee Osteoarthritis (OA) detection system from magnetic resonance (MR) images is proposed. This system is capable of detecting the presence of OA considering two classification categories: ‘non-OA’ and ‘OA’. OA is one of the most prevalent condition resulting to disability particularly in elderly population. OA is the most common articular disease of the developed world and a leading cause of chronic disability, mainly as a consequence of the knee OA and/or hip OA. Nowadays, medical images such as MR images are widely used for the OA diagnosis. For this, a medical specialist analyzes medical images by measuring the changes and in particular for knee OA, the changes in the compartment of the tibio-femoral cartilage. The proposed method consists mainly of both a data processing module and binary classification module, which process the 3-D data from MR images. In this study, we present a novel knee OA diagnostic approach that can identify the condition using magnetic resonance MR images using the Support Vector Machine (SVM) algorithm. Our suggested method is predicated on using 3-D data from MR scans of an actual cohort and the Independent Component Analysis (ICA) technique. The experimental results showed that our ICA-SVM machine learning model achieved 86% of testing accuracy with both 72% of specificity and 100% of sensitivity, once trained with a small MR image dataset. Furthermore, a benchmark evaluation was performed. The results suggest that using a larger and more diverse dataset could ensure the robustness of the proposed method. In future works, we will study the complementary use of ICA components from MR images and a convolutional neural network (CNN) to try to achieve better predictive rates in supervised learning using a larger dataset.