Researchers reported a machine-learning framework to optimize dual-target deep brain stimulation (DBS) aimed at both the subthalamic nucleus (STN) and the substantia nigra (SN) for Parkinson’s disease. The approach uses computational algorithms to configure stimulation more precisely than conventional targeting workflows, with the intent of improving clinical outcomes. Dual-target DBS is designed to broaden the effect profile across motor-circuit nodes, and the reported work centers on how an automated model can translate patient and targeting inputs into stimulation parameters. The update highlights the growing use of AI to refine neuromodulation delivery rather than relying solely on anatomical placement. The summary does not cite regulatory steps or clinical readouts beyond the described development, but it signals a direction for next-generation DBS personalization.