This study explored the potential of gait analysis as a tool for assessing post-injury complications, e.g., infection, malunion, or hardware irritation, in patients with lower extremity fractures. The research focused on the proficiency of supervised machine learning models predicting complications using consecutive gait datasets. We identified patients with lower extremity fractures at an academic center. Patients underwent gait analysis with a chest-mounted IMU device. Using software, raw gait data was preprocessed, emphasizing 12 essential gait variables. Machine learning models including XGBoost, Logistic Regression, SVM, LightGBM, and Random Forest were trained, tested, and evaluated. Attention was given to class imbalance, addressed using SMOTE. We introduced a methodology to compute the Rate of Change (ROC) for gait variables, independent of the time difference between gait analyses. XGBoost was the optimal model both before and after applying SMOTE. Prior to SMOTE, the model achieved an average test AUC of 0.90 (95% CI: [0.79, 1.00]) and test accuracy of 86% (95% CI: [75%, 97%]). Feature importance analysis attributed importance to the duration between injury and gait analysis. Data patterns showed early physiological compensations, followed by stabilization phases, emphasizing prompt gait analysis. This study underscores the potential of machine learning, particularly XGBoost, in gait analysis for orthopedic care. Predicting post-injury complications, early gait assessment becomes vital, revealing intervention points. The findings support a shift in orthopedics towards a data-informed approach, enhancing patient outcomes.

本研究探讨了步态分析作为评估下肢骨折患者的创伤后并发症，如感染、不正愈合或硬件刺激，的工具的潜力。研究关注了使用连续步态数据集预测并发症的监督式机器学习模型的效果。研究认为，机器学习，特别是XGBoost，在骨科护理中的步态分析中有潜力，预测创伤后并发症，早期步态评估变得至关重要，揭示干预点，结果支持对骨科学的数据驱动方法的转变，提高患者的疗效。

基于机器学习的步态分析在监测和管理下肢损伤中的重要性