Method and apparatus for adaptive neural interfaces

The predictive analytics pipeline addresses individual variations in neurostimulation by leveraging federated learning and graph theory to provide personalized and effective spinal cord stimulation, enhancing therapy outcomes and reducing hospital readmissions.

WO2026146221A1 Publication Date: 2026-07-09ONWARD MEDICAL NV
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Patent Information

Application Number
PCT/EP2026/050070
Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
Priority Date
2025-01-03
Filing Date
2026-01-05
Publication Date
2026-07-09

AI Technical Summary

Technical Problem

Existing electrical neurostimulation techniques face challenges in delivering personalized and effective treatment for spinal cord injuries due to individual anatomical and neurological variations, and the difficulty in predicting therapy effects and evolution.

Method used

A predictive analytics pipeline using federated learning and graph theory to analyze neural interface data, enabling personalized neurostimulation and neural sensing by generating machine learning models from decentralized data sources, allowing real-time adjustments and proactive therapy management.

Benefits of technology

Enables highly personalized and effective neurostimulation therapy by predicting patient-specific outcomes, improving motor function and reducing hospital readmissions, while ensuring data privacy and operational efficiency.

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Abstract

A method of predictive analysis of one or more characteristics associated with multiple neurostimulators used by respective multiple users, the method comprising: processing at a first site first input data collected from a first plurality of neurostimulation devices and / or neurostimulation users, and generating first machine learning model data from the first input data, the first machine learning model data comprising first model parameters; processing at a second site second input data collected from a second plurality of neurostimulation devices and / or neurostimulation users, and generating second machine learning model data from the second input data, the second machine learning model data comprising second model parameters; processing the first model parameters and the second model parameters to generate third machine learning model data representative of at least one neurostimulation characteristic; and using the third machine learning model data to predict evolution of at least one neurostimulation characteristic.
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