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Proceedings Paper

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

Paper Details

Date Published: 3 March 2017
PDF: 7 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 1013445 (3 March 2017); doi: 10.1117/12.2254344
Show Author Affiliations
Carlos D. Luna-Gómez, Instituto Tecnológico de Celaya (Mexico)
Laura A. Zanella-Calzada, Univ. Autónoma de San Luis Potosí (Mexico)
Miguel A. Acosta-García, Univ. Autónoma de Zacatecas (Mexico)
Jorge I. Galván-Tejada, Univ. Autónoma de Zacatecas (Mexico)
Carlos E. Galván-Tejada, Univ. Autónoma de Zacatecas (Mexico)
José M. Celaya-Padilla, Univ. Autónoma de Zacatecas (Mexico)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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