Applying Machine Learning to Gait Analysis Data for Hip Osteoarthritis Diagnosis

Area of Science:

• Biomechanical analysis
• Medical diagnostics
• Machine learning applications

Background:

• Hip osteoarthritis (OA) is a prevalent degenerative joint disease impacting a quarter of the population.
• Aging demographics are projected to increase OA prevalence.
• Gait analysis offers crucial biomechanical insights into OA-related pain and dysfunction.

Purpose of the Study:

• To differentiate gait patterns between hip OA patients and healthy individuals.
• To apply markerless motion capture and machine learning for gait data classification.
• To assess the efficacy of machine learning in identifying hip OA through gait analysis.

Main Methods:

• Analysis of gait parameters from 36 hip OA patients and 13 healthy controls.
• Training of machine learning models utilizing collected gait data.
• Utilizing markerless systems for non-invasive gait data acquisition.

Main Results:

• A Support Vector Classifier model achieved a high F1-score of 96% for classification.
• Demonstrated significant potential in distinguishing hip OA patients from controls based on gait.
• Highlighted the effectiveness of machine learning in analyzing complex gait data.

Conclusions:

• Machine learning applied to gait analysis can significantly enhance hip OA diagnosis.
• This approach holds promise for improving the clinical management of osteoarthritis.
• Integration of gait analysis and AI offers a novel diagnostic avenue for OA.