Implantable Neural Stimulation System with Closed-Loop AI Feedback
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Solution Overview
Problem
Conventional systems are inadequate for quantitatively detecting and tracking the progression of neurological diseases or the efficacy of treatments, as they fail to consider the initial state of neuronal brain regions and interplay, leading to undiagnosed cases and ineffective monitoring and stimulation.
Innovation Solution
A system comprising an implantable device with processors, stimulation devices, sensing devices, and communication technology that enables self-guided, self-directed diagnostics and treatment using artificial intelligence, capable of generating and transmitting stimulation signals and receiving sensed signals for dynamic closed-loop feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems are used, then device complexity is reduced, but measurement precision and diagnostic accuracy deteriorate because they cannot quantitatively detect and track neurological disease progression
Solution Approach 1:
The patent implements closed-loop feedback systems where neural activity data is continuously monitored, analyzed by AI algorithms, and used to dynamically adjust stimulation parameters. This feedback mechanism enables quantitative tracking of disease progression and treatment efficacy, resolving the contradiction between measurement precision and device complexity by automating the complex analytical functions.
Solution Approach 2:
The system incorporates self-guided, self-directed diagnostics and treatment capabilities where the implantable device autonomously monitors neural activity, processes data through embedded AI algorithms, and adjusts stimulation parameters without requiring constant external intervention. This self-service approach maintains high measurement precision while managing system complexity through autonomous operation.
2Reliability
If conventional treatment approaches are used, then ease of operation is maintained, but treatment efficacy deteriorates because they do not consider initial neuronal state and interplay
Solution Approach 1:
The system performs preliminary characterization of the initial neuronal state and interplay patterns before initiating treatment. By pre-analyzing baseline neural activity and establishing personalized treatment parameters based on individual neuronal characteristics, the system improves treatment efficacy while maintaining ease of operation through automated preliminary assessment and programming.
Solution Approach 2:
The patent dynamically adjusts stimulation parameters based on real-time neural activity data and AI analysis. Treatment parameters such as amplitude, frequency, and pulse width are continuously optimized according to measured neuronal responses, ensuring high treatment efficacy while the system handles parameter adjustments automatically to maintain operational simplicity.
3Adaptability or versatility
If static treatment protocols are used, then ease of operation is improved, but adaptability deteriorates because they cannot provide real-time adjustments based on disease progression
Solution Approach 1:
The system transitions from static treatment protocols to dynamic, real-time adaptive control. Neural activity monitoring and AI analysis continuously inform stimulation parameter adjustments, allowing the treatment to adapt to disease progression and individual patient responses. This dynamic approach is achieved through automated real-time processing, reducing the perceived control complexity for users.
Solution Approach 2:
Closed-loop feedback mechanisms enable the system to continuously monitor treatment responses and adjust parameters accordingly. The feedback from neural activity measurements is processed by AI algorithms that automatically modify stimulation settings, providing high adaptability while the automated feedback loop manages the complexity of real-time adjustments.
Data Source
AI summary
For example, in an embodiment, a system may comprise a processor, memory, and program instructions and data, a plurality of stimulation devices, program instructions and data stored to control the stimulation devices to generate and transmit stimulation signals, a plurality of sensing devices, program instructions and data to receive sensed signals from the sensing devices, a communication device adapted, wherein the processor, memory, plurality of stimulation devices, plurality of sensing devices, and communication device are contained in an implantable device adapted to be implanted in a body of a person, and an external computer system comprising a processor, memory, and program instructions and data, to perform dynamic closed loop feedback of the stimulation signals based on the received sensed signals to provide self-guided, self-directed diagnostics and treatment of neural conditions, wherein the external computer system is adapted to be attached to a body of the person.