Clinical Response Data Mapping for Deep Brain Stimulation
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Solution Overview
Problem
Current systems for selecting stimulation parameters for deep brain stimulation (DBS) and spinal cord stimulation (SCS) devices are inefficient and time-consuming, relying on trial-and-error methods without visual aids or computational models, making it difficult to predict the volume of tissue influenced by stimulation and often resulting in suboptimal therapy.
Innovation Solution
A system and method that generate and output a clinical response data map, allowing for the graphical representation of stimulation parameter settings in correlation with leadwire positions, enabling the selection of therapy parameters through a user interface that integrates therapy effect history, allowing for the visualization of therapeutic and adverse effects, and facilitating the selection of optimal stimulation parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If trial-and-error methods are used for parameter selection, then the system is simple to operate, but the time and cost required increase significantly
Solution Approach 1:
The system performs preliminary computational modeling to predict the volume of tissue influenced by stimulation before actual therapy begins. Virtual electrode implantations and computational models are prepared in advance to guide parameter selection, eliminating the need for time-consuming trial-and-error adjustments during patient treatment.
Solution Approach 2:
The system creates a virtual copy of the patient's anatomy and electrode configuration through computational modeling. This virtual model allows clinicians to test different parameter settings and visualize tissue activation patterns without physically adjusting electrodes in the patient, significantly reducing setup time while maintaining operational simplicity.
2Measurement precision
If visual aids and computational models are introduced, then measurement precision of tissue influence improves, but device complexity increases
Solution Approach 1:
The system introduces computational models and visual display interfaces as intermediaries between the physical electrode and the clinician's decision-making process. These intermediaries translate complex electrical field calculations into intuitive visual representations of tissue activation, improving measurement precision without requiring the physical stimulation system itself to become more complex.
Solution Approach 2:
The system replaces physical trial-and-error adjustment mechanisms with computational simulations and visual feedback systems. Instead of manually adjusting electrodes and observing patient responses, the system uses software-based models to predict outcomes, reducing the need for complex physical manipulation while enhancing precision.
3Reliability
If multiple stimulation parameters are optimized simultaneously, then therapy effectiveness improves, but the complexity of parameter selection increases
Solution Approach 1:
The system adds a visual dimension to parameter optimization by displaying multiple parameter settings and their effects simultaneously on a graphical interface. Instead of adjusting parameters sequentially or independently, the system presents a multi-dimensional view where clinicians can see how different parameter combinations affect tissue activation patterns, making complex optimization more manageable.
Solution Approach 2:
The computational modeling system serves multiple functions simultaneously: it predicts tissue activation, visualizes electric field distributions, compares different parameter settings, and guides optimal parameter selection. This multi-functional approach allows comprehensive parameter optimization without proportionally increasing system complexity, as a single integrated platform performs all these tasks.
Data Source
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AI summary
A system and method include a processor that, based on at least a subset of stored data of clinical effects of one or more stimulations of anatomical tissue performed using electrodes of an implanted leadwire, generates and outputs at least one graphical marking representing the at least the subset of the stored data. Each of the at least one graphical marking represents a respective portion of the at least the subset of the stored data and is output in association with a respective set of values for each of at least two parameters by which one or more the stimulations were performed. The markings are plotted in a graph defined by axes corresponding to values of respective stimulation parameters. Alternative, the markings are arranged in a column of a tabular report. The markings are two-toned to provide respective information for both therapeutic and adverse side effects.