Diagnostic Quadrant Construction for Neurodegenerative Disease Discrimination
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Solution Overview
Problem
Current methods for diagnosing neurodegenerative diseases such as Alzheimer's, Parkinson's, and dementia with Lewy body using single biomarkers like β-amyloid, tau protein, and α-synuclein are inadequate, leading to misdiagnosis due to overlapping concentrations in plasma, particularly failing to accurately distinguish between dementia with Lewy body and Alzheimer's disease.
Innovation Solution
A method involving the transformation of original biomarker concentrations into modified concentrations by calculating mean values and standard deviations, followed by positioning these in a frame of multiple biomarkers to find an optimal boundary separating quadrants corresponding to different diseases, utilizing multiple biomarkers like plasma α-synuclein, β-amyloid, and tau protein.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If single biomarker concentrations are used for diagnosis, then the diagnostic method is simple, but the diagnostic accuracy is insufficient leading to misdiagnosis
Solution Approach 1:
The patent combines multiple independent biomarkers (such as plasma α-synuclein, β-amyloid, and tau protein) into a unified diagnostic framework. By measuring and integrating the concentrations of these different biomarkers together, the method achieves superior diagnostic accuracy for distinguishing between neurodegenerative diseases like dementia with Lewy body and Alzheimer's disease, resolving the limitation of single biomarker approaches.
Solution Approach 2:
The patent transitions from one-dimensional single biomarker measurement to two-dimensional multi-biomarker analysis by constructing a diagnostic map where different biomarker concentrations are plotted against each other. This dimensional expansion allows for more nuanced differentiation of disease states that cannot be distinguished using single biomarker levels alone.
2Reliability
If multiple biomarkers are used for diagnosis, then the diagnostic accuracy is improved, but the method complexity increases
Solution Approach 1:
The patent transforms the raw concentration values of multiple biomarkers into a standardized diagnostic framework by applying mathematical transformations. The original distributed concentrations are converted to modified distributed concentrations through normalization processes, allowing for reliable comparison and integration of different biomarker types while maintaining diagnostic reliability.
Solution Approach 2:
By constructing a two-dimensional diagnostic map that plots modified concentrations of different biomarkers against each other, the patent creates a visual and analytical framework that simplifies the interpretation of multiple biomarker data. This dimensional representation transforms complex multi-parameter data into an intuitive spatial distribution that enhances diagnostic reliability.
3Measurement precision
If original biomarker concentrations are used directly, then the measurement process is straightforward, but the discrimination between diseases is insufficient
Solution Approach 1:
The patent applies parameter transformation by converting original biomarker concentrations into modified distributed concentrations through statistical normalization. This transformation process adjusts the data distribution to enhance the separation between different disease groups, improving discrimination capability while systematically processing the concentration data.
Solution Approach 2:
The patent performs preliminary data transformation and normalization before conducting the actual diagnostic comparison. By pre-processing the biomarker concentration data to create modified distributed concentrations, the method prepares the data in an optimal state for disease discrimination, enhancing the effectiveness of subsequent analytical steps.
Data Source
AI summary
The present invention relates to a method for constructing quadrants corresponding to different diseases in a frame of concentrations of multiple independent biomarkers, comprising:(a) transferring original distributed concentrations of every independent biomarker to modified distributed concentrations, comprising:calculating the mean value and the standard deviation of the original distributed concentrations for a given independent biomarker;individually subtracting all the original distributed concentrations by the mean value for the given independent biomarker; andindividually dividing all the differences by the standard deviation to get the modified distributed concentrations for the given independent biomarker;(b) positing the modified distributed concentrations in a frame of multiple independent biomarkers; and(c) finding a boundary optimally separating neighboring quadrants corresponding to different diseases.