3D User Interface for Neurostimulator Therapy Configuration
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Solution Overview
Problem
The process of configuring electrical stimulation therapy for patients with implantable neurostimulators is time-consuming and requires significant trial and error, especially with complex electrode array geometries, leading to challenges in optimizing therapy parameters and minimizing side effects.
Innovation Solution
A 3D user interface system that allows clinicians to define and manipulate stimulation fields within anatomical regions, automatically generating necessary parameters to approximate the desired field, reducing the complexity of selecting electrode combinations and polarities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual selection of electrode combinations and parameters is used, then the clinician can identify effective stimulation programs, but the process becomes time-consuming and tedious
Solution Approach 1:
The system performs preliminary computation of stimulation parameters, electrode combinations, and predicted outcomes before the clinician makes final selections. The processor pre-calculates optimal electrode configurations and presents them to the clinician, reducing the time required for manual trial-and-error programming while maintaining therapy optimization accuracy.
Solution Approach 2:
The processor acts as an intermediary between the clinician's therapeutic goals and the complex electrode configuration space. It translates clinical objectives into specific electrode combinations and stimulation parameters, eliminating the need for manual exploration of numerous electrode configurations while preserving clinical judgment and therapy effectiveness.
2Reliability
If multiple electrode combinations are tested to identify optimal therapy, then therapy efficacy is improved, but the complexity of the programming process increases
Solution Approach 1:
The processor serves as an intermediary that manages the complexity of testing multiple electrode combinations. It automatically evaluates numerous electrode configurations, predicts their therapeutic outcomes, and presents the most promising options to the clinician, thereby maintaining high therapy efficacy while reducing programming complexity to a manageable level.
Solution Approach 2:
The system replaces the manual mechanical process of testing electrode combinations with automated computational analysis. The processor performs rapid simulations and predictions of stimulation outcomes, substituting the clinician's manual trial-and-error approach with algorithm-driven optimization, thus reducing programming complexity while improving therapy efficacy.
3Loss of information
If manual specification of electrode combinations is used, then the clinician can observe immediate efficacy and side effects, but the process requires significant trial and error
Solution Approach 1:
The system performs preliminary prediction of efficacy and side effects for different electrode combinations before the clinician tests them. The processor simulates and forecasts clinical outcomes, allowing the clinician to prioritize the most promising electrode configurations for actual testing, thereby reducing the number of trials needed while preserving valuable clinical feedback.
Solution Approach 2:
The system incorporates feedback loops where actual clinical observations of efficacy and side effects are used to refine and update the prediction models. The processor learns from real-world clinical feedback and improves its ability to predict outcomes, creating a continuous improvement cycle that enhances programming efficiency while maintaining accurate clinical assessment.
Data Source
AI summary
The disclosure describes a method and system that allows a user to configure electrical stimulation therapy by defining a three-dimensional (3D) stimulation field. After a stimulation lead is implanted in a patient, a clinician manipulates the 3D stimulation field in a 3D environment to encompass desired anatomical regions of the patient. In this manner, the clinician determines which anatomical regions to stimulate, and the system generates the necessary stimulation parameters. In some cases, a lead icon representing the implanted lead is displayed to show the clinician where the lead is relative to the 3D anatomical regions of the patient.