Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

Carlos D. Luna-Gómez; Laura A. Zanella-Calzada; Jorge I. Galván-Tejada; Carlos E. Galván-Tejada; José M. Celaya-Padilla

doi:10.1117/12.2254344

3 March 2017 Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

Carlos D. Luna-Gómez, Laura A. Zanella-Calzada, Jorge I. Galván-Tejada, Carlos E. Galván-Tejada, José M. Celaya-Padilla

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 1013445 (2017) https://doi.org/10.1117/12.2254344
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Citation Download Citation

Carlos D. Luna-Gómez, Laura A. Zanella-Calzada, Jorge I. Galván-Tejada, Carlos E. Galván-Tejada, and José M. Celaya-Padilla "Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013445 (3 March 2017); https://doi.org/10.1117/12.2254344

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