Machine learning in deep brain stimulation: A systematic review.

Area of Science:

• Neurosurgery
• Artificial Intelligence
• Computational Neuroscience

Background:

• Deep Brain Stimulation (DBS) is a prevalent therapy for neurological disorders, particularly movement disorders.
• The application of Machine Learning (ML) in medicine, including neurosurgery, is rapidly expanding.
• A comprehensive review of ML applications in DBS was previously lacking.

Purpose of the Study:

• To systematically review and analyze the current use of ML in Deep Brain Stimulation (DBS).
• To identify trends, common methodologies, data utilization, and validation strategies in ML for DBS.
• To provide insights into how ML is advancing functional neurosurgery.

Main Methods:

• A systematic methodology was employed to gather a corpus of 73 relevant research papers.
• Each paper was analyzed to identify clinical applications, data characteristics, ML methods, and validation approaches.
• Analysis included 12 distinct sub-categories of ML application and validation in DBS.

Main Results:

• The reviewed papers demonstrate diverse applications of ML across the DBS workflow.
• Identified trends include evolving ML techniques, common data frameworks, and persistent limitations.
• ML is contributing to addressing complex clinical issues within DBS.

Conclusions:

• ML is significantly impacting the field of DBS, offering novel computational approaches.
• While progress has been made, many challenges in ML for DBS remain open for further research and development.
• The integration of ML is pushing the boundaries of functional neurosurgery, with ongoing potential for innovation.