Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics.

ABSTRACT:

Objective

Detection of early osteoarthritis (EOA) of the knee is crucial for effective management and improved outcomes. This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA.

Methods

Total 43 manufacturing workers (86 legs) aged 40-70 years were evaluated. The participants were categorized using an Early Osteoarthritis Questionnaire. Single-leg standing kinematics was captured using 2D video analysis to assess the horizontal displacement of six

SUBMITTER: Hwang UJ 

PROVIDER: S-EPMC11907616 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature