Automated image registration for knee pain prediction in osteoarthritis: Data from the OAI

Abstract

Diagnose Knee osteoarthritis (OA) is a very important task, in this work an automated metrics method is used to predict chronic pain. In early stages of OA, changes into joint structures are shown, some of the most common symptoms are; formation of osteophytes, cartilage degradation and joint space reduction, among others. Using public data from the Osteoarthritis initiative (OAI), a set of X-ray images with different Kellgren Lawrence score (K & L) scores were used to determine a relationship between bilateral asymmetry and the radiological evaluation in K & L score with the chronic knee pain. In order to measure the asymmetry between the knees, the right knee was registered to match the left knee, then a series of similarity metrics; mutual information, correlation, and mean square error were computed to correlate the deformation (mismatch) and K & L score with chronic knee pain. Radiological information was evaluated and scored by OAI radiologist groups, all metric of image registration were obtained in an automated way. The results of the study suggest an association between image registration metrics, radiological K & L score with chronic knee pain. Four GLM models wit AUC 0.6 and 0.7 accuracy random forest classification model was formed with this information to classify the early bony changes with OA chronic knee pain.