Vein-Segmented Spectral Training for Accurate Analyte Testing
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Solution Overview
Problem
Current non-invasive analyte testing methods, particularly for blood glucose, suffer from inaccuracies due to the significant impact of non-analyte components in spectral data, leading to discomfort and inefficiencies in traditional optical testing.
Innovation Solution
A method and system utilizing a convolutional neural network model trained with spectral data from distinct areas of the skin, such as veins and non-veins, to differentiate and exclude non-analyte influences, combined with fluorescence spectroscopy for accurate analyte concentration measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional optical testing methods are used for non-invasive analyte testing, then testing can be performed without invasion, but measurement precision deteriorates due to significant impact of non-analyte components in spectral data
Solution Approach 1:
The patent divides the spectral data acquisition process into multiple distinct areas (vein area and non-vein area) and collects spectral data from each area separately. This segmentation allows the system to differentiate between signals from the analyte (in veins) and non-analyte components (in non-vein areas), thereby improving measurement precision while maintaining non-invasive operation.
Solution Approach 2:
The patent extracts and removes the influence of non-analyte components from the spectral data by comparing and subtracting the spectral characteristics obtained from non-vein areas from those obtained from vein areas. This extraction process eliminates interfering signals from non-analyte components, significantly improving the accuracy of analyte concentration measurement.
2Measurement precision
If spectral data from multiple areas is collected and processed, then non-analyte interference is reduced, but device complexity increases
Solution Approach 1:
The patent employs a single spectral acquisition device that performs multiple functions: collecting spectral data from both vein and non-vein areas, processing the data to eliminate non-analyte interference, and providing accurate analyte concentration measurements. This multi-functional approach improves testing accuracy without proportionally increasing device complexity.
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 enhances testing accuracy by reducing non-analyte interference, enabling non-invasive, real-time, and cost-effective analyte testing with improved comfort and precision.
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
obtaining spectral data that indicate uneven distribution of a reflection signal or an excitation signal generated by an analyte when irradiated by light
Data Source
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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.