Implanted Analyte Sensor End-of-Life Prediction by Real-Time Assessment
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Solution Overview
Problem
Conventional systems fail to accurately predict the end of functional life of implanted sensors due to varying body responses to implanted devices, making precise lifetime prediction difficult.
Innovation Solution
An analyte monitoring system that assesses sensor performance in real time, utilizing information on device performance and body response, including a transmitter and sensor with photodetectors and indicators, to predict sensor delamination and end of life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems use fixed lifetime predictions for implanted sensors, then device complexity is reduced, but prediction accuracy deteriorates due to varying body responses
Solution Approach 1:
The system continuously monitors sensor performance parameters (signal quality, sensitivity, baseline drift) and uses this feedback to dynamically update the end-of-life prediction. This closed-loop approach allows the prediction to adapt to actual sensor degradation patterns and individual body responses, resolving the contradiction between simple fixed predictions and accurate dynamic predictions
Solution Approach 2:
The sensor system performs self-diagnosis by monitoring its own performance characteristics and automatically generating end-of-life predictions without external intervention. The sensor evaluates its own degradation state through built-in performance metrics and communicates this information to the receiver, enabling autonomous lifetime assessment
2Measurement precision
If the system continuously monitors sensor performance in real time, then prediction accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous monitoring, the system performs performance assessments at periodic intervals. The sensor evaluates key performance parameters at scheduled times and uses these periodic measurements to update degradation models and predictions, reducing energy consumption while maintaining adequate prediction accuracy
Solution Approach 2:
The system monitors only the most critical performance parameters necessary for accurate prediction (signal quality, sensitivity changes, baseline drift) rather than all possible sensor characteristics. This selective monitoring approach achieves sufficient prediction accuracy with minimal energy expenditure
3Reliability
If the system uses multiple performance parameters for assessment, then prediction reliability is improved, but device complexity increases
Solution Approach 1:
The sensor system uses a multi-functional performance assessment mechanism that simultaneously evaluates multiple degradation indicators (signal quality, sensitivity, baseline drift) through a unified monitoring framework. This universal approach allows single sensor readings to provide information about multiple degradation modes, improving prediction reliability without proportionally increasing system complexity
Solution Approach 2:
The system combines multiple performance parameters into an integrated degradation model that synthesizes information from various sensor metrics. By merging these parameters into a cohesive assessment framework, the system achieves reliable multi-factor evaluation while maintaining manageable complexity through unified processing
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate prediction of sensor end of life by continuously monitoring sensor performance and body response, triggering alarms for sensor retirement and detecting delamination.
Implementation Method 1
a photodetector that generates a signal in response to emission light
Data Source
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AI summary
An analyte monitoring system and method. The analyte monitoring system may include an analyte sensor and a transmitter. The analyte sensor may include an indicator element that exhibits one or more detectable properties based on an amount or concentration of an analyte in proximity to the indicator element. The transmitter may be configured to receive measurement information from the analyte sensor. The transmitter may be configured to assess in real time a performance of the analyte sensor based on at least the measurement information. The transmitter may be configured to determine whether the performance of the analyte sensor is deficient based at least on the assessed performance of the analyte sensor. The transmitter may be configured to predict an amount of time remaining before the performance of the analyte sensor becomes deficient based on at least the assessed performance of the analyte sensor.