Prediction of clinical effects of deep brain stimulators (DBS)
A machine-learning model predicts optimal stimulation parameters for DBS systems, addressing non-optimal electrode placement and parameter selection issues, enhancing treatment efficacy and reducing side effects.
WO2026128196A1PCT designated stage Publication Date: 2026-06-18BOSTON SCI NEUROMODULATION CORP
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- BOSTON SCI NEUROMODULATION CORP
- Filing Date
- 2025-11-20
- Publication Date
- 2026-06-18
Smart Images
Figure US2025056380_18062026_PF_FP_ABST
Abstract
A system may include a neurostimulator including electrodes and a processing system configured to perform a process. The process may include receiving lead placement information and clinical history for the patient. The clinical history may include both first clinical effect data corresponding to times when the patient is not being treated with a therapy and second clinical effect data corresponding to times when the patient is being treated with the therapy. The process may include employing at least one trained machine-learning model to provide predicted clinical effects, corresponding to untested stimulation parameter sets for the neurostimulator, based on the lead placement information, the first clinical effect data and the second clinical effect data. A parameter set may be identified for use in delivering a therapy based at least in part on the predicted clinical effects for the untested stimulation parameter sets.
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