Image Registration Measures and Chronic Osteoarthritis Knee Pain Prediction: Data from the Osteoarthritis Initiative - Galván-Tejada, J. I.; Galván Tejada, C. E.; López-Monteagudo, F. E.; Alonso-González, O.; Moreno-Báez, A.; Celaya-Padilla, J. M.; Zanella-Calzada, L. A. (Revista Mexicana de Ingenieria Biomedica, 2018)
Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up.