In this study, a gray level co-occurrence matrix (GLCM) based 3D Texture Analysis method was utilized for early prediction of knee osteoarthritis using 3D DESS images. Twenty subjects were extracted from the Osteoarthritis Initiative (baseline) with Kellgren-Lawrence (KL) score = 0 at baseline. Ten of the selected subjects developed the disease and showed KL ≥ 2 at the 36-month visit. Knee DESS images were analyzed using various quantization schemes and three machine learning models were trained based on the output GLCM features. Naïve Bayes model trained on tibial features showed the highest accuracy (86.8%) for OA onset after 36 months.
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