AI Article Synopsis

  • - Electromyography (EMG) is being explored as a way to predict the severity of knee osteoarthritis (OA) by analyzing muscle activation patterns during walking, linked to patient-reported measures like WOMAC and VAS.
  • - This study collected EMG data from the lower leg muscles of 84 patients with advanced knee OA to analyze how muscle activity and co-contraction relate to functional limitations experienced by these patients.
  • - Using machine-learning models, the researchers found high accuracy (coefficient of determination) in predicting WOMAC and VAS scores based on muscle activity, revealing that greater muscle co-contraction correlates with more severe OA symptoms.

Article Abstract

Electromyography (EMG) is considered a potential predictive tool for the severity of knee osteoarthritis (OA) symptoms and functional outcomes. Patient-reported outcome measures (PROMs), such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analog scale (VAS), are used to determine the severity of knee OA. We aim to investigate muscle activation and co-contraction patterns through EMG from the lower extremity muscles of patients with advanced knee OA patients and evaluate the effectiveness of an interpretable machine-learning model to estimate the severity of knee OA according to the WOMAC (pain, stiffness, and physical function) and VAS using EMG gait features. To explore neuromuscular gait patterns with knee OA severity, EMG from rectus femoris, medial hamstring, tibialis anterior, and gastrocnemius muscles were recorded from 84 patients diagnosed with advanced knee OA during ground walking. Muscle activation patterns and co-activation indices were calculated over the gait cycle for pairs of medial and lateral muscles. We utilized machine-learning regression models to estimate the severity of knee OA symptoms according to the PROMs using muscle activity and co-contraction features. Additionally, we utilized the Shapley Additive Explanations (SHAP) to interpret the contribution of the EMG features to the regression model for estimation of knee OA severity according to WOMAC and VAS. Muscle activity and co-contraction patterns varied according to the functional limitations associated with knee OA severity according to VAS and WOMAC. The coefficient of determination of the cross-validated regression model is 0.85 for estimating WOMAC, 0.82 for pain, 0.85 for stiffness, and 0.85 for physical function, as well as VAS scores, utilizing the gait features. SHAP explanation revealed that greater co-contraction of lower extremity muscles during the weight acceptance and swing phases indicated more severe knee OA. The identified muscle co-activation patterns may be utilized as objective candidate outcomes to better understand the severity of knee OA.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11139965PMC
http://dx.doi.org/10.1038/s41598-024-63266-7DOI Listing

Publication Analysis

Top Keywords

severity knee
20
knee
13
advanced knee
12
knee severity
12
patient-reported outcome
8
outcome measures
8
muscle co-activation
8
knee osteoarthritis
8
severity
8
muscle activation
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!