AI Calibration Model for Analyte Sensor Accuracy
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Solution Overview
Problem
Conventional calibration apparatuses for analyte sensors require frequent recalibration due to increased measurement errors over time, primarily because they rely on simple mapping tables or linear functions, which are not effective in accounting for temporal changes.
Innovation Solution
A method and apparatus using an artificial intelligence (AI) calibration model, specifically a non-linear model, to predict calibration values and calculate prediction uncertainty, allowing for extended calibration cycles by incorporating measured analyte data from both reference and sensor devices, and generating calibration requests only when necessary based on preset thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple mapping table or linear function is used for calibration, then the calibration process is simple and easy to implement, but the measurement error increases over time and frequent recalibration is required
Solution Approach 1:
The patent changes the calibration model from simple linear functions to non-linear models that can adapt to temporal changes in sensor characteristics. The system dynamically adjusts calibration parameters based on measured deviations, allowing the calibration model to evolve with sensor degradation and environmental changes, thereby maintaining measurement accuracy over extended periods.
Solution Approach 2:
The patent introduces dynamic calibration by continuously monitoring measurement deviations and updating calibration values in real-time. The system transitions from static calibration tables to dynamic models that adapt to changing sensor performance, enabling the calibration process to respond to temporal variations in sensor characteristics without requiring frequent manual recalibration.
2Measurement precision
If frequent recalibration is performed to maintain measurement accuracy, then the measurement precision is maintained, but the loss of time and increased operational burden increase
Solution Approach 1:
The patent performs preliminary calibration using non-linear models and prediction algorithms before measurement errors become significant. By establishing accurate calibration relationships in advance and using prediction models to estimate future calibration needs, the system maintains measurement precision without requiring frequent actual recalibration operations, thereby reducing time loss and operational burden.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor measurement deviations and use this information to update calibration values. The system measures actual analyte concentrations, compares them with sensor readings, and uses the deviations to refine calibration parameters, creating a closed-loop system that maintains precision automatically without manual intervention for frequent recalibration.
3Measurement precision
If a non-linear AI calibration model is used to predict calibration values, then the calibration accuracy is improved and calibration cycle is extended, but the device complexity and computational requirements increase
Solution Approach 1:
The patent replaces traditional mechanical or tabular calibration methods with AI-based non-linear models implemented in software. The system uses machine learning algorithms to capture complex sensor behavior patterns that cannot be represented by simple mathematical functions, achieving superior calibration accuracy while maintaining flexibility through software-based solutions rather than hardware complexity.
Solution Approach 2:
The patent transforms the calibration approach by changing from fixed calibration tables to dynamic AI models that can adapt parameters based on input conditions. The non-linear models adjust calibration parameters continuously based on sensor readings and environmental factors, providing accurate calibration values without requiring complex hardware modifications or manual calibration procedures.
Data Source
AI summary
Disclosed are an apparatus and method for calibrating analyte data. In an embodiment, a method of calibrating analyte data may include receiving first analyte data measured by a reference device, storing the received first analyte, calculating a calibration value by using an artificial intelligence (AI) calibration model having second analyte data measured by an analyte sensor and the stored first analyte data as inputs, and calculating the final analyte data by incorporating the calculated calibration value into the second analyte data.