Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning.
Journal:
International journal of computer assisted radiology and surgery
PMID:
38411781
Abstract
PURPOSE: Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be replaced by machine learning approaches. This paper explores the feasibility of using deep reinforcement learning (DRL) in this context, starting with the single-electrode use-case of deep brain stimulation (DBS).