The Application of Machine Learning to Predict Clinical Outcomes of Deep Brain Stimulation in Parkinson's Disease: A Systematic Review.

Background: Parkinson's disease (PD) is a degenerative condition of the nervous system that is primarily characterized by a gradual decline of motor function. For patients with suboptimal response to medical treatment, deep brain stimulation (DBS) is a well-recognized surgical approach. This systematic review evaluates the performance of machine learning (ML) models in classifying patients or symptoms or to predict postoperative outcomes following DBS in PD. Methods: PubMed, Scopus, Cochrane, Embase, and Web of Science were searched in accordance with PRISMA through December 31, 2024. We included original human studies of DBS-treated PD in which ML used clinical (non-imaging) features to classify patients or symptoms, or to predict postoperative outcomes. Cohort, cross-sectional, and case-series designs were eligible. Imaging-based prediction studies were excluded. Results: From 961 records, eight studies (n=555 patients) met the inclusion criteria. Three studies performed preoperative-to-postoperative outcome prediction, and five focused on symptom or patient classification. Targets included motor severity, speech outcomes, and gait-related measures. The Support Vector Machine (SVM) was the most frequently applied ML model, followed by the k-nearest neighbor, which was used in three studies. Commonly used assessment tools included the Mini-Mental State Examination (MMSE), the Hoehn and Yahr Scale, and the Unified Parkinson's Disease Rating Scale (UPDRS). Conclusion: This review highlights early but exploratory application of ML for patients' or symptoms classification and predicting clinical outcomes and adverse events following DBS using preoperative clinical data. However, the current evidence is sparse, single-center, and methodologically heterogeneous, with limited external validation. Therefore, clinical translation remains premature.