Auxiliary Diagnostic Apparatus Using Spectral Error Vectors
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Solution Overview
Problem
Current diagnostic methods for biological tissues using spectral properties rely heavily on visual inspection by doctors, lacking a quantitative approach to determine affected areas, which is subjective and experience-dependent.
Innovation Solution
An auxiliary diagnostic apparatus and method employing multiple linear regression analysis and indicator calculation to extract a quantitative indicator from spectral property vectors, utilizing error vectors to determine the presence and type of affected areas in biological tissues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual checking by doctor is used for diagnosis, then diagnostic experience can be utilized, but quantitative judgment material cannot be obtained and reliability is insufficient
Solution Approach 1:
The patent replaces the mechanical/visual inspection system with an automated computational system. The analysis unit automatically executes multiple linear regression analysis on spectral property vectors to calculate indicator values, substituting the doctor's visual checking mechanism with an automated mathematical model that processes spectral data and generates quantitative diagnostic indicators.
Solution Approach 2:
The system performs self-diagnosis through automated computation. The analysis unit independently executes the multiple linear regression analysis using the spectral property vectors and individual component vectors, calculating indicator values without requiring external manual intervention. The system serves itself by automatically processing diagnostic data and generating results.
2Measurement precision
If visual inspection is used for diagnosis, then diagnostic process is simple, but measurement precision and objectivity are insufficient
Solution Approach 1:
The patent transforms the diagnostic approach by changing from qualitative visual inspection to quantitative parameter measurement. The system measures spectral property vectors and calculates indicator values through multiple linear regression analysis, transforming the diagnostic output from subjective visual assessment to objective quantitative parameters that can be precisely measured and compared.
Solution Approach 2:
The patent introduces spectral property vectors and indicator values as intermediary elements between the raw spectral data and the final diagnostic conclusion. The analysis unit processes the spectral property vectors through multiple linear regression analysis to generate indicator values, which serve as intermediary quantitative measures that objectively reflect the presence and type of affected areas, mediating between the complex spectral data and the diagnostic judgment.
3Extent of automation
If quantitative indicator calculation is implemented, then objective assessment can be obtained, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing individual component vectors corresponding to different tissue types (mucous membrane, epithelium, muscle, fat, bone) before actual diagnosis. These pre-computed vectors are stored in the analysis unit and can be directly used in the multiple linear regression analysis during diagnosis, eliminating the need to perform complex computations in real-time and reducing the computational burden during actual diagnostic operations.
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
Enables a quantitative judgment material for diagnosis, providing a reliable and objective assessment of affected areas based on spectral properties, reducing reliance on visual inspection and enhancing diagnostic accuracy.
Implementation Method 1
a spectral property (distribution of an optical absorption factor with respect to frequency) of a biological tissue, such as a mucous membrane of a digestive organ
Implementation Method 2
The spectral property depends on types of substances included in a surface layer of the biological tissue target for measurement of the spectral property (Beer-Lambert Law)
Data Source
AI summary
Disclosed is an auxiliary diagnostic apparatus including: a vector input unit that reads a test vector which is a spectral property vector of a biological tissue targeted for a diagnosis; a multiple linear regression analysis unit that executes a multiple liner regression analysis for the test vector with a plurality of individual component vectors which are spectral property vectors of particular substances, and obtains an error vector which is a vector of a residual error component; and an indicator calculation unit that extracts a feature of the error vector, and, from the extracted error vector, calculates an indicator representing whether an affected area is included in the biological tissue targeted for the diagnosis and which type of affected area has a possibility of being included in the biological tissue.