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An interpretable machine learning approach for predicting and grading hip osteoarthritis using gait analysis.

Qing Yang1, Xinyu Ji2, Yuyan Zhang2

  • 1Department of Breast and Thyroid Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jing Wu Wei Qi Road, Jinan, 250021, Shandong Province, People's Republic of China.

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|July 2, 2025
PubMed
Summary

Machine learning accurately detects hip osteoarthritis (OA) and its severity using lower extremity gait data. This method offers objective biomechanical insights for clinical assessment.

Keywords:
Gait analysisHip osteoarthritis (OA)Interpretability analysisMachine learningNonlinear featuresSpatiotemporal parameters

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Area of Science:

  • Biomechanics
  • Machine Learning
  • Orthopedics

Background:

  • Osteoarthritis (OA) of the hip causes stiffness and limited mobility, altering gait patterns in patients.
  • Current methods for tracking biomechanical changes in unilateral hip OA patients are not well-established.
  • ClinicalTrials.gov identifier: NCT01907503.

Purpose of the Study:

  • To evaluate the efficacy of lower extremity kinematic gait data for detecting and rating unilateral hip OA severity.
  • To utilize machine learning algorithms for analyzing gait patterns.

Main Methods:

  • Developed a feature extraction framework for spatiotemporal and nonlinear gait features.
  • Applied Shapley Additive exPlanations (SHAP) for feature selection and interpretation.
  • Utilized a support vector machine (SVM) to classify gait patterns between hip OA patients and healthy controls (HCs).
  • Validated the strategy on a public dataset of 80 HCs and 99 hip OA patients (KL Grades 2-4).

Main Results:

  • Achieved 98.21% accuracy for detecting hip OA (HCs vs. hip OA patients).
  • Attained 89.65% accuracy for severity rating (HCs vs. Grade 2/3 vs. Grade 4).
  • Achieved 87.54% accuracy for detailed severity rating (HCs vs. Grade 2 vs. Grade 3 vs. Grade 4).

Conclusions:

  • The proposed machine learning method demonstrates superior performance for hip OA detection and severity assessment.
  • Gait analysis provides objective biomechanical data, detecting subtle changes not visible radiographically.
  • This approach can supplement the Kellgren and Lawrence (KL) grading scale in clinical practice.