Stimulation Lead Programming via Field Model Visualization
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Solution Overview
Problem
The process of selecting electrode combinations for implantable electrical stimulators is time-consuming and requires significant trial and error, especially with complex electrode array geometries, which increases the burden on physicians and can lead to inefficiencies in delivering effective neurostimulation therapy while minimizing side effects.
Innovation Solution
A user interface that allows clinicians to visualize and manipulate electrical field models and activation fields, enabling the selection of electrode combinations and parameters through guided programming, including the use of stimulation templates and patient anatomy data to optimize therapy delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual electrode combination selection is used, then physician flexibility and clinical judgment are maintained, but the programming process becomes time-consuming and tedious
Solution Approach 1:
The system enables self-service programming by allowing the system to automatically generate and evaluate electrode combinations based on pre-set optimization criteria and patient-specific anatomical data, reducing the need for manual physician intervention in the tedious process of testing multiple combinations
Solution Approach 2:
The system performs preliminary actions by pre-calculating and ranking multiple electrode combinations based on predicted therapeutic efficacy before clinical application, allowing physicians to select from pre-evaluated options rather than performing trial-and-error testing during patient programming sessions
2Reliability
If the number of electrode combinations is increased to improve therapy optimization, then better therapeutic efficacy can be achieved, but the complexity of programming increases
Solution Approach 1:
The system incorporates feedback mechanisms by using patient-specific anatomical data from imaging studies to evaluate and rank electrode combinations, providing physicians with feedback on which combinations are most likely to be effective based on individual patient anatomy rather than requiring trial-and-error testing
Solution Approach 2:
The system manages complexity by changing parameters in a systematic way - using pre-set optimization criteria and automated algorithms to evaluate multiple electrode combinations based on varying parameters such as electrode polarity, amplitude, pulse width, and pulse rate, allowing comprehensive optimization without increasing programming complexity
3Reliability
If trial and error methods are used to identify optimal electrode combinations, then therapeutic efficacy can be optimized, but the number of clinic visits and programming time increase
Solution Approach 1:
The system performs preliminary evaluation of multiple electrode combinations using patient-specific anatomical data and pre-set optimization criteria before the clinical programming session, allowing physicians to select from pre-ranked options and significantly reducing the time needed during actual patient programming
Solution Approach 2:
The system creates virtual models and simulations of electrode combinations based on patient-specific anatomical data from imaging studies, allowing physicians to evaluate and select optimal combinations in silico before applying them to the actual patient, thereby reducing the need for multiple trial-and-error clinic visits
Data Source
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AI summary
The disclosure is directed to programming implantable stimulators to deliver stimulation energy via one or more implantable leads having complex electrode array geometries. A programmer is configured to generate an electrical field model from selected stimulation parameters and patient anatomy data. The electrical field model indicates how the electrical field propagation would occur in the patient during therapy. In addition, the programmer may be configured to generate an activation field model from the electrical field model and a neuron model. The activation field indicates which neurons within the electrical field will be activated during the therapy. Either of these field models may be presented to the user via a user interface that also displays a representation of the lead implanted within the patient. The user interface may allow the user to adjust the stimulation therapy by manipulating displayed field or activation model representations.