A machine-learning framework was developed to optimize deep brain stimulation (DBS) targeting both the subthalamic nucleus (STN) and substantia nigra (SN) for Parkinson’s disease. The dual-target approach uses computational optimization to configure stimulation more precisely than single-target strategies. Researchers describe the method as engineered around advanced algorithms that can adjust targeting for each patient and treatment goal, with the goal of improving clinical outcomes. DBS targeting accuracy is a major determinant of efficacy and adverse-effect profiles. The industry impact is twofold: software-driven personalization can raise the ceiling on existing device classes, and a validated dual-target planning workflow could become a repeatable standard for centers performing DBS. The brief didn’t provide trial phase or clinical outcome numbers, but it positions the development as a concrete targeting optimization step rather than a purely theoretical model.