Machine learning-based neuroimaging for prediction of deep brain stimulation outcomes in movement disorders: Systematic review and meta-analysis

Abstract Background: Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse generator that administers electrical stimulation to designated brain regions responsible for motor control. The preoperative identification of effective predictive factors is of utmost importance for appropriate patient selection. In this study, we evaluate the potential of machine learning-based neuroimaging for predicting DBS outcomes. (PROSPERO Registration: CRD420261279318) Method: Following the PRISMA statement, eligible studies were selected through searching three databases (PubMed, Scopus, Web of Science) on November 6, 2025. Methodological quality was assessed using the PROBAST+AI tool. Random-effects models pooled discrimination performance (AUC). Heterogeneity was investigated using meta-regressions for age and gender alongside subgroup analysis by type of algorithm. Publication bias was assessed using Egger regression test. Results: Twenty studies were included in the analysis. Most investigations focused on PD, STN-DBS, and postoperative motor improvement, while a smaller number assessed neuropsychiatric outcomes. Overall, the pooled discrimination for models predicting motor outcomes showed an AUC of 0.86, and the pooled models for delirium showed an AUC of 0.87. Regarding the risk of bias assessment, seven studies were classified as low risk, while thirteen were identified as high risk. Conclusion: Machine learning-based neuroimaging shows promising potential for preoperative prediction of DBS outcomes. However, the current literature is characterized by a persistent gap between encouraging discrimination and reliable clinical readiness. The main weakness of the field lies in analytical rigor and generalizability. These models should currently only be considered as promising research tools.