The role of machine learning–based neuroimaging in predicting DBS outcomes: Systematic review and meta-analysis

Deep brain stimulation (DBS) is an established treatment for movement disorders such as Parkinson’s disease, essential tremor, and dystonia. Patient response to DBS varies considerably, and predicting outcomes remains a challenge. Machine learning–based models applied to neuroimaging data have emerged as a promising approach to improve prediction of clinical outcomes. This systematic review and meta-analysis aims to synthesize current evidence on the predictive performance of these models to inform patient selection and optimize DBS outcomes.