ABSTRACT
Deep brain stimulation (DBS) is a surgical treatment for advanced Parkinson's disease (PD) that has undergone technological evolution that parallels an expansion in clinical phenotyping, neurophysiology, and neuroimaging of the disease state. Machine learning (ML) has been successfully used in a wide range of healthcare problems, including DBS. As computational power increases and more data become available, the application of ML in DBS is expected to grow. We review the literature of ML in DBS and discuss future opportunities for such applications. Specifically, we perform a comprehensive review of the literature from PubMed, the Institute for Scientific Information's Web of Science, Cochrane Database of Systematic Reviews, and Institute of Electrical and Electronics Engineers' (IEEE) Xplore Digital Library for ML applications in DBS. These studies are broadly placed in the following categories: (1) DBS candidate selection; (2) programming optimization; (3) surgical targeting; and (4) insights into DBS mechanisms. For each category, we provide and contextualize the current body of research and discuss potential future directions for the application of ML in DBS.
ABSTRACT
The emergence of motion sensors as a tool that provides objective motor performance data on individuals afflicted with Parkinson's disease offers an opportunity to expand the horizon of clinical care for this neurodegenerative condition. Subjective clinical scales and patient based motor diaries have limited clinometric properties and produce a glimpse rather than continuous real time perspective into motor disability. Furthermore, the expansion of machine learn algorithms is yielding novel classification and probabilistic clinical models that stand to change existing treatment paradigms, refine the application of advance therapeutics, and may facilitate the development and testing of disease modifying agents for this disease. We review the use of inertial sensors and machine learning algorithms in Parkinson's disease.
ABSTRACT
OBJECTIVE: High frequency stimulation (HFS) of the subthalamic nucleus (STN) is a well-established therapy for Parkinson's disease (PD), particularly the cardinal motor symptoms and levodopa induced motor complications. Recent studies have suggested the possible role of 60 Hz stimulation in STN-deep brain stimulation (DBS) for patients with gait disorder. The objective of this study was to develop a computational model, which stratifies patients a priori based on symptomatology into different frequency settings (i.e., high frequency or 60 Hz). METHODS: We retrospectively analyzed preoperative MDS-Unified Parkinson's Disease Rating Scale III scores (32 indicators) collected from 20 PD patients implanted with STN-DBS at Mount Sinai Medical Center on either 60 Hz stimulation (ten patients) or HFS (130-185 Hz) (ten patients) for an average of 12 months. Predictive models using the Random Forest classification algorithm were built to associate patient/disease characteristics at surgery to the stimulation frequency. These models were evaluated objectively using leave-one-out cross-validation approach. RESULTS: The computational models produced, stratified patients into 60 Hz or HFS (130-185 Hz) with 95% accuracy. The best models relied on two or three predictors out of the 32 analyzed for classification. Across all predictors, gait and rest tremor of the right hand were consistently the most important. CONCLUSIONS: Computational models were developed using preoperative clinical indicators in PD patients treated with STN-DBS. These models were able to accurately stratify PD patients into 60 Hz stimulation or HFS (130-185 Hz) groups a priori, offering a unique potential to enhance the utilization of this therapy based on clinical subtypes.
