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Machine Learning-Based Prognostic Prediction for Knee Osteoarthritis After High Tibial Osteotomy Using
Koji Iwasaki1, Kento Sabashi2, Hidenori Koyano3
1Department of Functional Reconstruction for the Knee Joint, Faculty of Medicine, Hokkaido University, Sapporo 060-8638, Japan.
Journal of Functional Morphology and Kinesiology
|March 28, 2026
Summary
Machine learning models using preoperative gait acceleration from inertial measurement units (IMUs) can predict knee osteoarthritis surgery outcomes. This approach aids in identifying high-risk patients for better surgical planning and personalized care.
Area of Science:
- Biomedical Engineering
- Orthopedics
- Machine Learning in Medicine
Background:
- Osteotomy around the knee (OAK) is a joint-preserving surgery for knee osteoarthritis (OA).
- Predicting clinical outcomes and identifying high-risk patients preoperatively remains a challenge.
- Inertial measurement units (IMUs) offer a potential tool for objective gait assessment.
Purpose of the Study:
- To develop and validate a machine learning model for predicting clinical outcomes after OAK.
- To utilize preoperative gait acceleration data from IMUs for outcome prediction.
- To assess the potential of IMU-derived features for preoperative risk stratification.
Main Methods:
- A multicenter prospective study enrolled 67 patients undergoing OAK.
- Preoperative gait was recorded using synchronized IMUs on the lumbar spine and tibia.
- Wavelet-based time-frequency features from tibial acceleration were extracted and analyzed using a Random Undersampling Boost classifier.
Main Results:
- The machine learning model achieved an Area Under the Curve (AUC) of 0.744, with a sensitivity of 0.69 and specificity of 0.72.
- Key predictors included gait acceleration magnitude and variability in specific frequency bands during stance phases.
- No significant baseline demographic or radiographic differences were observed between good and poor outcome groups.
Conclusions:
- Preoperative IMU-derived gait acceleration features demonstrate moderate-to-good discrimination for predicting OAK outcomes.
- This approach can aid in preoperative risk stratification for patients undergoing OAK.
- The findings support individualized perioperative management strategies based on objective gait analysis.
