Researchers presented a machine learning framework to optimize dual-target deep brain stimulation (DBS) in Parkinson’s disease, aiming to better target both the subthalamic nucleus (STN) and substantia nigra (SN). The approach focuses on computationally selecting stimulation parameters for improved clinical outcomes. The excerpt frames the work as an evolution beyond single-target strategies, leveraging algorithmic optimization to refine how DBS is delivered. In practice, dual-targeting could help address variability in patient anatomy and symptom profiles. If further validated in clinical studies, the system could support more personalized DBS planning and execution, aligning with the broader push to make neuromodulation less “one-size-fits-all.”