Using explainable AI to identify disease-relevant and deep brain stimulation treatment-sensitive gait features in Parkinson's disease.

Research overview

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BACKGROUND: Gait impairment is a hallmark motor deficit of Parkinson’s disease (PD) and represents an important, yet insufficiently understood, target of subthalamic deep brain stimulation (DBS). Although DBS can improve several motor symptoms, identifying robust and physiologically meaningful gait biomarkers that capture both disease-related deficits and stimulation-induced improvements remains a major challenge. In particular, conventional mean-based gait metrics often fail to fully characterize pathological gait or treatment responsiveness. METHODS: We analyzed 35 spatiotemporal gait parameters obtained during continuous walking from individuals with PD assessed before and after subthalamic DBS, alongside age-matched healthy controls. Multiple machine learning classifiers were evaluated to discriminate between groups, with extreme gradient boosting (XGBoost) achieving the best performance. To enhance interpretability and reduce redundancy among correlated parameters, grouped SHapley Additive exPlanations (SHAP) were applied to rank feature importance and guide feature selection. RESULTS: Feature selection consistently highlighted step width variability, step width asymmetry, bilateral interlimb coordination, and the anteroposterior margin of stability as the most discriminative parameters. A compact set of five overlapping features after selection not only reliably distinguished PD gait from healthy controls but also demonstrated a shift toward healthy ranges following DBS. Importantly, these selected features outperformed conventional mean-based metrics in capturing both pathological gait characteristics and treatment-related changes. DISCUSSION: Our findings demonstrate that explainable artificial intelligence approaches can identify physiologically grounded gait features that may serve as candidate markers of both PD severity and DBS responsiveness. By emphasizing variability, asymmetry, coordination, and dynamic stability measures, this approach moves beyond traditional mean-based metrics and provides more sensitive markers of neuromodulation effects. These results support the use of interpretable machine learning frameworks to enable more precise evaluation of DBS outcomes and may contribute to individualized patient management strategies in Parkinson’s disease.

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