Machine learning for the localization of Subthalamic Nucleus during deep brain stimulation surgery: a systematic review and Meta-analysis

Introduction Delineating subthalamic nucleus (STN) boundaries using microelectrode recordings (MER) and trajectory history is a valuable resource for neurosurgeons, aiding in the accurate and efficient positioning of deep brain stimulation (DBS) electrodes within the STN. Here, we aimed to assess the application of artificial intelligence, specifically Hidden Markov Models (HMM), in the context of STN localization. Methods A comprehensive search strategy was employed, encompassing electronic databases, including PubMed, EuroPMC, and MEDLINE. This search strategy entailed a combination of controlled vocabulary (e.g., MeSH terms) and free-text keywords pertaining to “artificial intelligence,” “machine learning,” “deep learning,” and “deep brain stimulation.” Inclusion criteria were applied to studies reporting the utilization of HMM for predicting outcomes in DBS, based on structured patient-level health data, and published in the English language. Results This systematic review incorporated a total of 14 studies. Various machine learning compared wavelet feature to proposed features in diagnosing the STN, with the HMM yielding a diagnostic odds ratio (DOR) of 838.677 (95% CI: 203.309–3459.645). Similarly, the K-Nearest Neighbors (KNN) model produced parameter estimates, including a diagnostic odds ratio of 25.151 (95% CI: 12.270–51.555). Meanwhile, the support vector machine (SVM) model exhibited parameter estimates, with a DOR of 13.959 (95% CI: 10.436–18.671). Conclusions MER data demonstrates significant variability in neural activity, with studies employing a wide range of methodologies. Machine learning plays a crucial role in aiding STN diagnosis, though its accuracy varies across different approaches.