Neuromodulation System Personalized Spinal Cord Stimulation
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Solution Overview
Problem
Current neuromodulation systems for treating spinal cord injuries lack patient-specific optimization of electrode placement and stimulation parameters, relying on invasive and time-consuming trial-and-error methods, which are often ineffective and unsafe.
Innovation Solution
A neuromodulation system utilizing a multi-layer computational framework for designing personalized stimulation protocols, incorporating image thresholding, Kalman-filtering, and specific algorithms to reconstruct patient anatomy and simulate neuromodulation effects, enabling optimal electrode configuration and stimulation parameters for specific nerve fiber recruitment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If trial-and-error methods are used to determine electrode placement and stimulation parameters, then some level of treatment can be achieved, but the process becomes invasive, time-consuming, and unsafe
Solution Approach 1:
The system performs preliminary computational simulations to determine optimal electrode placement and stimulation parameters before actual clinical application. The multi-layer computational framework predicts treatment outcomes in silico, allowing clinicians to implement the pre-determined optimal configuration directly without time-consuming trial-and-error procedures in the patient.
Solution Approach 2:
The system creates a virtual copy of the patient's spinal cord anatomy and physiology through computational modeling. This digital twin allows for risk-free testing and optimization of electrode placement and stimulation parameters, replacing invasive trial-and-error methods with non-invasive computational predictions.
2Reliability
If general neuromodulation protocols are applied without patient-specific optimization, then treatment can be implemented quickly, but efficacy is reduced due to anatomical variations
Solution Approach 1:
The system tailors the neuromodulation protocol to each patient's specific anatomical and physiological characteristics. By incorporating patient-specific spinal cord geometry, tissue conductivity, and nerve fiber distribution from imaging data, the computational framework generates localized optimization for each individual rather than applying uniform protocols.
Solution Approach 2:
The system automatically adjusts multiple stimulation parameters (amplitude, pulse width, frequency, electrode positions) based on computational predictions of optimal configurations for each patient. This automated parameter optimization handles the complexity of personalization without requiring manual intervention, making the process both effective and efficient.
3Measurement precision
If invasive procedures are used to test electrode placement, then accurate positioning can be achieved, but patient safety and comfort are compromised
Solution Approach 1:
The system replaces invasive physical testing with non-invasive computational simulations. By creating accurate virtual models of the patient's spinal cord and running simulations to predict electrode placement accuracy, the system achieves precise positioning without exposing the patient to surgical risks or uncomfortable trial procedures.
Solution Approach 2:
The computational framework acts as an intermediary between imaging data and electrode placement decisions. It processes anatomical information and predicts optimal configurations without requiring direct manipulation or testing on the patient's body, eliminating the need for invasive exploration while maintaining high precision.
Data Source
AI summary
A neuromodulation system comprising:at least one input means for inputting patient data into the neuromodulation system;at least one model calculation and building means for building a patient model, the patient model describing the anatomy and/or physiology and/or pathophysiology and the real and/or simulated reaction of the patient on a provided and/or simulated neuromodulation;at least one computation means for using the patient model (M) and calculating the impact of the provided and/or simulated neuromodulation.The present invention further relates to a method for providing neuromodulation.