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

被引:2
作者
Luna-Gomez, Carlos D. [1 ]
Zanella-Calzada, Laura A. [2 ]
Acosta-Garcia, Miguel A. [3 ]
Galvan-Tejada, Jorge I. [3 ]
Galvan-Tejada, Carlos E. [3 ]
Celaya-Padilla, Jose M. [3 ]
机构
[1] Inst Tecnol Celaya, Antonio Garcia Cubas 600 Fovissste, Celaya, Mexico
[2] UnivAutonoma San Luis Potosi, Lateral Av Salvador Nava S-N Lomas, San Luis Potosi, Mexico
[3] Univ Autonoma Zacatecas, Ramon Lopez Velarde 801 Ctr, Zacatecas, Mexico
来源
MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS | 2017年 / 10134卷
基金
美国国家卫生研究院;
关键词
Osteoarthritis; pain; MOAKS; knee pain; genetic algorithm; MANAGEMENT;
D O I
10.1117/12.2254344
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
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.
引用
收藏
页数:7
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