Abstract
Knee osteoarthritis (KOA) is characterized by pain and decreased gait function. We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. The study included 375 volunteers with variable KOA grades. The severity of KOA was determined using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). WOMAC scores were used to classify disease severity into three groups. A total of 1087 features were extracted from the gait data. An ANOVA and student’s t-test were performed and only features that were significant were selected for inclusion in the machine learning algorithm. Three WOMAC subscales (physical function, pain and stiffness) were further divided into three classes. An ANOVA was performed to determine which selected features were significantly related to the subscales. Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. Forty-three features were selected based on ANOVA and student’s t-test results. The following number of features were selected from each joint: 12 from hip, 1 feature from pelvic, 17 features from knee, 9 features from ankle, 1 feature from foot, and 3 features from spatiotemporal parameters. A significance level of < 0.0001 and < 0.00003 was set for the ANOVA and t-test, respectively. The physical function, pain, and stiffness subscales were related to 41, 10, and 16 features, respectively. Linear regression models showed a correlation of 0.723 and the machine learning algorithm showed a correlation of 0.741. The severity of KOA was predicted by gait analysis features, which were incorporated to develop an objective estimation model for KOA severity. The identified features may serve as a tool to guide rehabilitation and progress assessments. In addition, the estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation.
Highlights
Knee osteoarthritis (KOA) is characterized by pain and decreased gait function
Since gait dysfunction can be evaluated objectively using this method, it has been suggested as an alternative tool for measuring patient d isabilities8,14,17. Previously[18], we identified an association between gait analysis features and KOA radiological grade and showed successful estimation of the KellgrenLawerence (KL) grade using a machine learning algorithm based on key gait features
Information is limited regarding the relationship between patient reported outcome measures (PROMs) and kinetic and kinematic gait features
Summary
We aimed to find KOA-related gait features based on patient reported outcome measures (PROMs) and develop regression models using machine learning algorithms to estimate KOA severity. An ANOVA was performed to determine which selected features were significantly related to the subscales Both linear regression models and a random forest regression was used to estimate patient the WOMAC scores. The estimation model presented here suggests an approach for clinical application of gait analysis data for KOA evaluation. We have extracted as many feature from gait data including both traditional and engineering methods from multiple joints. We anticipate that WOMAC estimation model based on gait feature would explain the biomechanical difference between the severity of KOA and provide further understanding for the relationship between KOA and gait function
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
