Patient-Specific Neurostimulation Optimization Using Baseline and Implant Data
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Solution Overview
Problem
Existing medical devices for electrical nerve stimulation, such as those used for urinary incontinence, face challenges in determining optimal therapy settings due to patient variability and the need for customized treatment, leading to inefficiencies and increased power consumption.
Innovation Solution
A system and method for determining personalized neurostimulation therapy settings using patient-specific and population-informed information, integrating data from implantable and external devices with cloud computing to optimize stimulation program settings over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional trial-and-error method is used to determine stimulation settings, then therapeutic effectiveness may be achieved, but time consumption and number of office visits increase
Solution Approach 1:
The system performs preliminary actions by collecting patient-specific information (demographics, medical history, anatomy) and population-informed information (aggregate data from similar patients) before actual therapy delivery. This pre-processing of data enables the algorithm to generate optimized stimulation settings in advance, eliminating the need for multiple trial-and-error office visits and significantly reducing the time to achieve therapeutic effectiveness.
Solution Approach 2:
The system creates a digital model or copy of the patient's physiological characteristics by processing their specific information and population data. This digital twin allows the optimization algorithm to simulate and determine effective stimulation settings virtually before applying them to the actual patient, thereby reducing the need for repeated physical adjustments during office visits.
2Adaptability or versatility
If customized stimulation settings are determined through multiple office visits, then patient-specific therapy is achieved, but device complexity and resource requirements increase
Solution Approach 1:
The system enables self-service by implementing an automated optimization algorithm that processes patient information and population data to determine personalized stimulation settings without requiring extensive manual intervention from healthcare providers. The algorithm autonomously analyzes the data, generates optimized settings, and can be implemented through a processor in the medical device itself, reducing the need for complex external programming systems and multiple adjustment visits.
Solution Approach 2:
The system achieves patient-specific customization by dynamically changing stimulation parameters (amplitude, pulse width, frequency, electrode configuration) based on the optimization algorithm's analysis of patient-specific and population data. Rather than requiring a complex system to manually adjust each parameter through multiple visits, the algorithm simultaneously optimizes all parameters based on processed information, simplifying the overall system architecture while maintaining high adaptability.
3Reliability
If continuous stimulation is provided, then therapeutic effect is maintained, but power consumption increases
Solution Approach 1:
The optimization algorithm determines optimized stimulation settings that can be delivered in periodic or intermittent fashion rather than continuous stimulation. By analyzing patient-specific response patterns and population data, the system identifies optimal timing and duration for stimulation episodes, maintaining therapeutic effectiveness while reducing overall power consumption of the implantable device.
Data Source
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AI summary
A system may receive first information relating to a patient captured during a baseline period that is prior to the patient receiving stimulation. The system may receive second information relating to the patient captured during an initial therapy assignment. The second information may include testing data generated by delivering stimulation during an implant procedure. The system may determine initial stimulation program settings based on the first information, the second information and population-informed information. The population-informed information may be related to other patients. The system may cause, during a training period, delivery of therapy based on the initial stimulation program settings.