3D Knee Kinematic Parameters Effectively Diagnose Knee Osteoarthritis and Assess Its Therapeutic Strategy

Abstract

Knee osteoarthritis (KOA) is a worldwide disease leading to knee function loss and disorders. However, traditional assessment by X-ray cannot assess patients' knee functions and disorders dynamically, making it impossible to achieve a direct functional assessment of KOA. To solve this problem, here it is shown that 3D knee gait parameters could be used to diagnose KOA and guide its therapeutic strategy through direct functional assessment. We employ a total of 1201 participants, and successfully build and validate diagnostic and predictive models for KOA diagnosis and therapeutic strategy using an artificial intelligence (AI)-based method, logistic regression, a kind of interpretable machine learning. Four diagnostic models are successfully established including angular (AM), translational (TM), composite (CM), and ATCM (a parallel conjoint model of AM, TM, and CM) model with a Youden index of 0.7312, 0.6689, 0.8214, and 0.7492, respectively. The same AI-based method is also used to develop medical decision classification (MDC) for predicting whether a KOA patient needs operative intervention or not. MDC has a Youden index, sensitivity, and specificity of 0.8886, 92.11%, and 96.75%, respectively. These findings contribute to new knowledge of knee kinematics and KOA diagnosis and represent a new approach to accurate KOA diagnosis and assessment.