Dimensionality Reduction for Disease Classification Visualization
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Solution Overview
Problem
Current methods for diagnosing Philadelphia-negative myeloproliferative neoplasms (Ph-MPN) face challenges in distinguishing between overlapping disorders due to subjective histological features and the lack of reliable tools for accurate classification, particularly in cases without detectable 'driver' mutations, leading to diagnostic ambiguity.
Innovation Solution
A computer-implemented method using dimensionality reduction algorithms to generate visual representations of disease-relevant classifications in a reduced parameter space, allowing for more informative comparisons of biological samples by transforming N-dimensional data into 2D or 3D spaces, facilitating the identification of disease-specific patterns and changes over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subjective histological features are used for classification, then diagnostic flexibility is maintained, but measurement precision and reliability deteriorate
Solution Approach 1:
The patent replaces subjective manual histological assessment with automated computational image analysis systems. Machine learning algorithms process morphological and topological features of cells, substituting the human expert's mechanical/subjective evaluation with an automated computational system that provides objective, reproducible measurements while maintaining diagnostic accuracy.
Solution Approach 2:
The patent transforms categorical histological descriptions into quantifiable morphological and topological parameters. By measuring specific cellular features (size, shape, nuclear characteristics) and their spatial relationships, the system converts subjective visual assessments into precise numerical data that can be objectively compared and analyzed.
2Loss of information
If N-dimensional data representation is used, then information completeness is improved, but ease of operation and visualization deteriorate
Solution Approach 1:
The patent applies dimensionality reduction techniques to transform high-dimensional morphological and topological data into lower-dimensional visual representations. This allows the system to preserve essential diagnostic information from N-dimensional parameter spaces while projecting it onto 2D or 3D visualization spaces that are interpretable by clinicians, effectively bridging the gap between information completeness and visualizability.
3Reliability
If comprehensive morphological assessment is performed, then diagnostic accuracy is improved, but measurement time and complexity increase
Solution Approach 1:
The patent implements automated computational analysis that continuously processes comprehensive morphological and topological features without interruption. The system performs complete quantitative assessments of all relevant cellular parameters simultaneously through algorithmic processing, eliminating the sequential, time-consuming nature of manual evaluation while maintaining thoroughness.
Solution Approach 2:
The system performs self-assessment through automated image analysis algorithms that independently evaluate morphological and topological characteristics without requiring continuous human intervention. The computational system processes and interprets all diagnostic features autonomously, providing comprehensive assessment results much faster than manual methods while maintaining reliability.
Data Source
AI summary
Methods and systems for generating visual representations of variation of disease-relevant classification are disclosed. Training data is received that comprises sample data units from subjects that represent information about a biological sample via an N-dimensional set of values. A dimensionality reduction algorithm represents each sample data unit as a respective point in a reduced dimension parameter space. Distributions of points from the dimensionality reduction are used to derive a probability density distribution for each of a plurality of disease-relevant classifications in the reduced dimension parameter space. A visual representation of each of the derived probability density distributions in the reduced dimension parameter space is generated to provide a visual representation of disease-relevant classification variation over the parameter space.