Algorithmic Therapy Field Model for Medical Device Parameter Adjustment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical devices for delivering therapy, such as electrical stimulation and therapeutic agents, face challenges in optimizing therapy parameter settings to ensure effective treatment with minimal side effects, as existing methods lack precise control over the therapy field and efficiency.
Innovation Solution
The implementation of an algorithmic model of a therapy field that compares and adjusts therapy parameter values based on a reference field, allowing for real-time modification of therapy programs to enhance efficacy and efficiency while maintaining therapeutic outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If therapy parameter values are adjusted to optimize treatment efficacy, then treatment effectiveness improves, but device complexity and programming difficulty increase
Solution Approach 1:
The system pre-calculates and stores optimal therapy parameter sets corresponding to different therapy field configurations before actual therapy delivery. This allows the device to automatically select pre-optimized parameters based on the desired therapy field shape, avoiding complex real-time calculations and simplifying programming while maintaining treatment efficacy.
Solution Approach 2:
The patent introduces an intermediate therapy field model that serves as a mediator between the desired therapeutic outcome and the actual therapy parameter settings. This model allows clinicians to specify therapy field characteristics (shape, size, orientation) without directly manipulating complex electrical parameters, thereby simplifying the programming interface while achieving optimized treatment efficacy through automatic parameter calculation.
2Measurement precision
If therapy parameters are modified to improve treatment precision, then therapy field control improves, but energy consumption increases
Solution Approach 1:
The system calculates therapy parameters with higher precision than strictly necessary for basic functionality, but only applies the precision level required for the specific therapy field configuration being used. This avoids unnecessary computational energy consumption while maintaining adequate control precision for each particular therapy scenario.
Solution Approach 2:
The patent dynamically adjusts therapy parameters including amplitude, pulse width, and frequency based on the selected therapy field model. By changing these parameters according to the specific field configuration requirements, the system achieves precise therapy field control when needed while avoiding excessive energy consumption by using minimal necessary parameter adjustments for each scenario.
3Adaptability or versatility
If multiple therapy parameter sets are stored for different conditions, then adaptability improves, but device memory requirements and complexity increase
Solution Approach 1:
The patent segments the therapy parameter storage into hierarchical levels: commonly used therapy field models are stored in the implantable device with compact representations, while less frequently used or highly customized parameter sets are stored externally in a library accessible via programming interface. This segmentation reduces the memory burden on the implantable device while maintaining comprehensive adaptability across different therapy conditions.
Solution Approach 2:
The system designs therapy parameter sets with universal structures that can accommodate multiple therapy conditions through configurable parameters. Rather than storing completely separate parameter sets for each condition, the patent uses a universal parameter framework where a single storage structure serves multiple therapeutic applications, thereby reducing overall storage requirements while maintaining versatility.
Data Source
AI summary
Techniques for modeling therapy fields for therapy delivered by medical devices are described. Each therapy field model is based on a set of therapy parameters and represents where therapy will propagate from the therapy system delivering therapy according to the set of therapy parameters. Therapy field models may be useful in guiding the modification of therapy parameters. As one example, a processor compares an algorithmic model of a therapy field to a reference therapy field and adjusts at least one therapy parameter based on the comparison. As another example, a processor adjusts at least one therapy parameter to increase an operating efficiency of the therapy system while substantially maintaining the modeled therapy field.