Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
School of Rehabilitation Medicine, Southern Medical University, Guangzhou, China.
J Neuroeng Rehabil. 2024 Apr 3;21(1):45. doi: 10.1186/s12984-024-01337-6.
Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms.
This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost).
Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability.
Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.
膝骨关节炎（KOA）是一种不可逆的退行性疾病，其特征为疼痛和异常步态。放射学通常用于检测 KOA，但存在局限性。本研究旨在确定与放射学膝骨关节炎（ROA）相关的足底压力变化，并使用机器学习算法对其进行验证。
本研究纳入了 92 名 KOA 程度不同的参与者。使用改良 Kellgren-Lawrence 量表将参与者分为非 ROA 和 ROA 组。总特征集包括 210 个动态足底压力特征，由可穿戴式鞋内系统采集，以及年龄、性别、身高、体重和体重指数。过滤和包装方法确定了最佳特征，用于训练五种类型的机器学习分类模型进行进一步验证：k-最近邻（KNN）、支持向量机（SVM）、随机森林（RF）、AdaBoost 和极端梯度提升（XGBoost）。
年龄、左外侧跟骨下峰值足底压力的标准差（f_L8PPP_std）、右第二峰值压力的标准差（f_Rpeak2_std）和右足中足压心前后位移变异的标准差（f_RYcopstd_std）与 ROA 最相关。具有 82.61%准确率和 0.8000 F1 分数的 RF 模型具有最佳的泛化能力。
足底压力的动态变化是有前途的机械生物标志物，可区分非 ROA 和 ROA。将可穿戴式鞋内系统与机器学习相结合，可以实现 KOA 的动态监测，有助于指导治疗计划。
