Zhou Peng, Liu Zhenyan, Dai Jiang, Yang Ming, Sui He, Huang Zhaoshu, Li Yu, Song Lingling
Department of Imaging, The Affiliated Hospital of Guizhou Medical University, No.28, Beijing Road, Yunyan District, Guiyang, 550000, Guizhou Province, China.
Department of Imaging, Dejiang County People's Hospital, Tongren, 565299, China.
Sci Rep. 2025 May 25;15(1):18163. doi: 10.1038/s41598-025-02786-2.
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.
膝关节相关疾病是全球主要的健康问题，是导致疼痛和行动不便的主要原因，在老年人中尤为如此。在临床医学中，膝关节疾病的精确识别和分类对于早期诊断和有效治疗至关重要。本研究提出了一种新方法，利用K近邻（KNN）算法结合多模态磁共振成像（MRI）技术，特别是mDxion-Quant（mDQ）和T2映射（T2m）来识别髌下脂肪垫（IFP）病变。这些成像方法提供了诸如脂肪分数（FF）、T2*和T2值等定量参数。通过特征工程构建了一组派生特征，以更好地捕捉IFP内的变化。这些特征用于训练KNN模型以对膝关节状况进行分类。所提出的方法在训练数据集和测试数据集上分别达到了94.736%和92.857%的分类准确率，优于CNN-Class8基准。该技术在膝关节疾病的早期检测、监测疾病进展和评估手术后结果方面具有巨大的临床潜力。
