Neural Network Test Strip Analysis for Cross-Device Analyte Measurement
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Solution Overview
Problem
Existing methods for determining analyte concentration in bodily fluids, such as blood glucose, face challenges due to variations in image recording and processing conditions across different devices, leading to inconsistent and inaccurate results.
Innovation Solution
A neural network model is generated through machine learning using images from multiple devices with varying configurations to analyze color transformations on test strips, allowing for consistent analyte concentration determination across diverse devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If image recording and processing are performed using multiple devices with different configurations, then the system can analyze color transformations on test strips, but the results become inconsistent and inaccurate due to variations in recording conditions
Solution Approach 1:
The patent applies parameter changes by transforming image data from multiple devices with different configurations into a unified representation. The system adjusts and normalizes imaging parameters (exposure, white balance, color space) to create consistent input data for the neural network, thereby maintaining measurement precision across diverse devices while preserving adaptability.
Solution Approach 2:
The patent introduces an intermediary processing layer that acts as a mediator between the diverse image inputs and the analyte concentration determination. This intermediary layer includes image preprocessing modules and normalization algorithms that harmonize data from different devices before it reaches the neural network, resolving the inconsistency problem while maintaining versatility.
2Reliability
If a neural network model is trained using data from multiple devices with varying configurations, then the model can account for device-specific variations, but the complexity of data processing and model training increases
Solution Approach 1:
The patent applies preliminary action by performing image preprocessing and feature extraction before the data reaches the neural network. The system pre-processes images from multiple devices to extract standardized color transformation features, reducing the complexity of subsequent model training while ensuring reliable and consistent analyte concentration readings across different devices.
Solution Approach 2:
The patent segments the complex data processing task into distinct modules: image acquisition, image preprocessing, color transformation analysis, and neural network inference. This segmentation allows each module to handle specific aspects of the data flow independently, making the overall system more manageable and easier to train while maintaining high reliability.
3Ease of manufacture
If traditional methods are used for determining analyte concentration, then the process is simple, but the results are inaccurate due to unaccounted device variations
Solution Approach 1:
The patent replaces traditional mechanical or manual measurement methods with a neural network-based computational system. This substitution enables the system to automatically learn and compensate for device variations from training data, achieving high measurement precision while maintaining ease of operation through automated processing.
Solution Approach 2:
The neural network model performs self-service by automatically learning device-specific characteristics from training data and adapting its own parameters to account for variations. This self-learning mechanism eliminates the need for manual calibration or complex user input, maintaining simplicity while dramatically improving measurement precision.
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
The model provides accurate and consistent analyte concentration readings by accounting for device-specific variations, enhancing precision and reliability.
Implementation Method 1
images for a region of interest of one or more test strips, the images being indicative of a color transformation of the region of interest in response to applying one or more first samples of a bodily fluid containing an analyte to the region of interest
Data Source
AI summary
A method for generating a module configured to determine concentration of an analyte in a sample of a body fluid is disclosed. The method includes providing a first set of measurement data derived from images of one or more test strips indicating a color transformation in response to a body fluid containing an analyte. The images can be recorded by multiple devices with differing cameras, software and/or hardware device configurations for image recording and image data processing. A neural network model can be generated in a machine learning process applying an artificial neural network and a module configured to determine concentration of an analyte in a second sample of a body fluid can be generated. Further, the present disclosure includes a system for generating the module as well as a method and a system for determining concentration of an analyte in a sample of a bodily fluid.