Fully automated shape analysis for detection of Osteoarthritis from lateral knee radiographs

Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people around the world. Since no cure has been discovered and considering the financial impact on health systems, any attempt to understand more of this disease could reveal new insights that would help develop new therapies. Lateral knee radiographs are often ignored both by clinicians and the research community when trying to diagnose OA or other diseases that affect the knee joint. Our goal is to show that this view has a considerable potential. We present a fully automated method based on a Random Forest Regression Voting Constrained Local Model (RFCLM) to discriminate radiographs of people that have developed OA from people who have not. The experiments involved models built on different combinations of the four shapes (patella, tibia, medial and lateral femoral condyles) of the knee joint. We show that automated analysis of the lateral view achieves classification performance comparable if not better than similar techniques applied to the frontal view.