Spectral Model Training for Non-Invasive Analyte Testing Accuracy
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Solution Overview
Problem
Current non-invasive analyte testing methods, particularly for blood glucose, suffer from inaccuracies due to the influence of non-analyte components in spectral data, leading to discomfort and inefficiencies.
Innovation Solution
A method and system utilizing a convolutional neural network model to process spectral data from infrared and ultraviolet imaging, distinguishing between areas with and without blood vessels to isolate analyte-specific spectral data, and employing a fluorescence spectroscopy approach to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional optical methods are used for non-invasive blood glucose testing, then testing can be performed without invasive procedures, but measurement precision is poor due to non-analyte interference
Solution Approach 1:
The patent divides the skin imaging area into multiple regions of interest (ROIs), including blood vessel areas and non-blood vessel areas. By segmenting the imaging area, the system can selectively analyze spectral data from different regions, focusing on areas with higher analyte concentration while excluding areas with non-analyte interference, thereby improving measurement precision while maintaining non-invasive operation.
Solution Approach 2:
The patent applies different processing methods to different regions of the skin surface. Blood vessel areas are processed to extract analyte-specific spectral characteristics, while non-blood vessel areas are processed to identify and remove non-analyte components. This local differentiation allows the system to maximize measurement precision in relevant areas while maintaining overall non-invasive operation.
2Productivity
If spectral data from entire skin area is used, then data collection is simple, but measurement precision deteriorates due to non-analyte component interference
Solution Approach 1:
The patent extracts and removes non-analyte components from the spectral data through a dedicated processing module. By identifying spectral signatures of non-blood vessel areas and subtracting their contributions, the system isolates the analyte-specific spectral information. This extraction process maintains data collection efficiency while significantly improving measurement precision by eliminating interfering non-analyte signals.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between raw spectral data and final analyte concentration calculation. This intermediary module includes algorithms to identify, separate, and remove non-analyte components, allowing the system to maintain simple data collection while achieving high measurement precision through computational mediation.
3Measurement precision
If multiple imaging areas are analyzed, then measurement precision improves through better analyte distribution analysis, but device complexity increases
Solution Approach 1:
The patent designs a multi-functional processing system that can handle multiple imaging areas and spectral data types through a unified framework. The same core algorithms and processing modules are applied consistently across different regions, allowing the system to maintain high measurement precision through multi-area analysis while avoiding proportional increases in device complexity. The universal processing approach scales efficiently with the number of analyzed areas.
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
This approach reduces the impact of non-analyte components, enabling accurate, non-invasive analyte testing with improved comfort and efficiency, particularly for glucose detection, by leveraging the uneven distribution of analytes in skin areas.
Implementation Method 1
spectral data that indicate uneven distribution of a reflection signal or an excitation signal generated by an analyte when irradiated by light
Implementation Method 2
spectral data that indicate uneven distribution of a reflection signal or an excitation signal generated by an analyte when irradiated by light
Implementation Method 3
employing a fluorescence spectroscopy approach to enhance accuracy
Data Source
AI summary
The present invention provides a model training method and system for analyte testing, a medium, and a device which relate to the field of optical analysis. The method includes: data obtaining: obtaining spectral data that indicate uneven distribution of a reflection signal or an excitation signal generated by an analyte when irradiated by light, and obtaining a true test result of the analyte in a same time period; and model training: using the obtained spectral data as an input of the testing model, using the true test result as an output of the testing model, and training the testing model. According to this application, the obtained spectral data can be prevented from being affected by the influence of a non-analyte, improving the accuracy of the testing model.