Neuromodulation Targeting System with Anatomical GUI
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Solution Overview
Problem
Conventional Spinal Cord Stimulation (SCS) systems face challenges in finding an optimal location for neuromodulation, as the process is often ad-hoc and trial-and-error, due to the complexity of anatomy and variation in fiber diameter responses to different neurostimulation parameters.
Innovation Solution
A neuromodulation targeting system that includes a graphical user interface (GUI) with an interactive display of patient anatomy, allowing user-selectable anatomic-specific inputs associated with predefined neural pathways. This system computationally determines a target region for neuromodulation therapy, distinct from the anatomic location of the input, and configures the therapy delivery to produce a localized clinical effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional trial-and-error programming is used to find optimal neuromodulation location, then the system can eventually achieve pain relief, but the process requires excessive time and multiple adjustments
Solution Approach 1:
The system performs preliminary computational determination of the target region based on patient anatomy and pain location before actual neuromodulation therapy delivery. The targeting selector engine calculates the optimal target region using pre-stored anatomical data and neural pathway information, eliminating the need for time-consuming trial-and-error programming during patient treatment.
Solution Approach 2:
The patent introduces an intermediary computational system (targeting selector engine) that mediates between the patient's pain location and the actual neuromodulation target. This intermediary component uses anatomical models and neural pathway data to translate pain location information into precise targeting parameters, removing the need for direct trial-and-error adjustment.
2Measurement precision
If neuromodulation is targeted directly to pain location, then the treatment addresses the symptomatic area, but anatomical complexity and fiber diameter variation cause stimulation of non-target fibers and side effects
Solution Approach 1:
The system applies local quality by delivering neuromodulation therapy to a specific target region that is distinct from the pain location. The targeting selector engine determines precise targeting parameters based on the patient's anatomy and the specific neural pathway involved, ensuring that stimulation is localized to the correct fiber population and avoids adjacent structures that could cause side effects.
Solution Approach 2:
The patent introduces an additional spatial dimension to the targeting process. Instead of directly stimulating the pain location, the system calculates a target region offset from the pain location based on anatomical relationships and neural pathway geometry. This dimensional transformation allows precise targeting of the intended fibers while avoiding non-target structures.
3Measurement precision
If detailed anatomical modeling and computational determination are implemented, then targeting precision is improved, but the system complexity increases
Solution Approach 1:
The targeting selector engine serves multiple functions: it determines target region, calculates targeting parameters, selects appropriate neural pathways, and adapts to different patient anatomies. By consolidating these functions into a single computational module with access to pre-stored anatomical data, the system achieves high precision without proportionally increasing overall system complexity.
Solution Approach 2:
Complex anatomical modeling and neural pathway data are prepared and stored in advance in a database. The targeting selector engine retrieves and processes this pre-prepared information computationally to determine target regions, rather than performing complex real-time calculations during therapy delivery. This preliminary preparation reduces the computational burden during actual treatment.
Data Source
AI summary
A neuromodulation targeting system includes a GUI that facilitates selection of one or more neuromodulation target regions. The GUI provides an interactive display representing anatomy of a patient with user-selectable portions corresponding to a plurality of predefined anatomical regions associated with distinct localized clinical effects of neuromodulation. The system further includes a targeting selector engine that is responsive to user selection of a first portion of the interactive display by configuring delivery of neuromodulation therapy to a first target region to produce a first localized clinical effect in the patient at a location corresponding to the first portion of the display, upon administration of the neuromodulation therapy to the patient.