Abstract
Knee-related disorders represent a major global health concern and are a leading cause of pain and mobility impairment, particularly in older adults. In clinical medicine, the precise identification and classification of knee joint diseases are essential for early diagnosis and effective treatment. This study presents a novel approach for identifying infrapatellar fat pad (IFP) lesions using the K-Nearest Neighbor (KNN) algorithm in combination with multimodal Magnetic Resonance Imaging (MRI) techniques, specifically mDxion-Quant (mDQ) and T2 mapping (T2m). These imaging methods provide quantitative parameters such as fat fraction (FF), T2*, and T2 values. A set of derived features was constructed through feature engineering to better capture variations within the IFP. These features were used to train the KNN model for classifying knee joint conditions. The proposed method achieved classification accuracies of 94.736% and 92.857% on the training and testing datasets, respectively, outperforming the CNN-Class8 benchmark. This technique holds substantial clinical potential for the early detection of knee joint pathologies, monitoring disease progression, and evaluating post-surgical outcomes.