Biological Sample Classification via IR Spectral Library Matching
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Solution Overview
Problem
Current methods for diagnosing biological samples, such as cytology and histopathology, rely heavily on subjective visual inspection and are prone to false positives and negatives, with spectral methods like FFT-based infrared imaging being noise-limited and costly.
Innovation Solution
The development of systems and methods that apply algorithms to images of biological samples for classification, using techniques like thresholding, oversampling, and probability adjustments to improve accuracy, and employing IR spectral signatures for data comparison and standardization across platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If spectral methods like FFT-based infrared imaging are used for biological sample analysis, then diagnostic information can be obtained, but the results are noise-limited and costs increase
Solution Approach 1:
The patent replaces traditional FFT-based spectral analysis with a library-based spectral matching approach. Instead of using mathematical transformation methods (FFT) that are sensitive to noise, the system uses direct comparison of spectral libraries with measured spectra, which is more robust to noise and provides more reliable diagnostic information.
Solution Approach 2:
The patent changes the analytical approach from frequency-domain analysis (FFT) to spectral library matching in the original spectral domain. This parameter change in the analysis method reduces noise sensitivity while maintaining diagnostic information quality.
2Loss of information
If spectral methods like FFT-based infrared imaging are used for biological sample analysis, then diagnostic information can be obtained, but costs increase
Solution Approach 1:
The patent replaces complex FFT-based spectral analysis systems with a simpler library-matching approach that requires less computational overhead and can be implemented with more affordable hardware, thereby reducing costs while maintaining diagnostic information quality.
Solution Approach 2:
The patent uses pre-acquired spectral libraries as reference copies for comparison with patient samples. This approach eliminates the need for complex real-time spectral decomposition and allows use of more cost-effective imaging systems while maintaining diagnostic accuracy.
3Ease of operation
If classical cytology and histopathology methods are used, then visual inspection of cells can be performed, but subjective interpretation leads to false positives and negatives
Solution Approach 1:
The patent replaces subjective visual inspection by pathologists with automated spectral library matching analysis. The system objectively compares patient spectra against reference libraries using computational algorithms, eliminating human subjectivity and reducing false positives and negatives while maintaining ease of operation.
Solution Approach 2:
The system enables self-diagnosis capability by automatically comparing patient spectra with reference libraries and generating diagnostic results without requiring subjective human interpretation, thereby improving reliability while keeping the system easy to operate.
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
These methods enhance the accuracy and objectivity of biological sample classification, reduce noise, and standardize IR signatures for consistent clinical representation, improving diagnostic efficiency and reducing costs.
Implementation Method 1
employing IR spectral signatures for data comparison and standardization across platforms
Data Source
AI summary
Methods, systems, and devices for classifying a biological sample that include receiving an image of a biological sample and applying one or more algorithms from a data repository to the image, generating a classification of the biological sample based on the outcome of the one or more algorithms applied to the image, and transmitting the classification for presentation on a display or via another medium. The methods, systems, and devices may also include features for developing a data master reference and/or other correlation/translation features to enable comparison of data sets from one platform to another or from one machine to another or from the same machine at different points in time.