Constructal Analysis of Vascular Networks for Disease Diagnosis
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Solution Overview
Problem
Current methods lack the capability to effectively analyze and quantify the structure and behavior of seemingly random patterns and pathways in systems with flow fields, such as blood vessels or river networks, which hinders predictive capabilities and comparison across different conditions.
Innovation Solution
A system and method utilizing constructal analysis, involving image processing and statistical measures like tortuosity and optical flow, to characterize and compare random patterns and networks, enabling the assessment of health and disease diagnosis in biological systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional analysis methods are used on random patterns, then the analysis process is simple, but the measurement precision and predictive capability are insufficient
Solution Approach 1:
The patent segments the random pattern analysis into multiple distinct processing stages: image acquisition, preprocessing, feature extraction, statistical analysis, and predictive modeling. Each stage handles specific aspects of the analysis, allowing complex measurements to be broken down into manageable components that can be optimized independently.
Solution Approach 2:
The patent introduces intermediate computational structures including feature vectors, statistical descriptors, and predictive models that serve as mediators between the raw image data and final predictions. These intermediaries transform complex random patterns into quantifiable metrics that can be analyzed with precision.
2Reliability
If detailed statistical measures are calculated for random patterns, then the predictive capability improves, but the computational time and complexity increase
Solution Approach 1:
The patent performs preliminary processing including image preprocessing, feature extraction, and statistical descriptor calculation before the actual predictive analysis. By preparing the data in advance and organizing it into structured formats, the subsequent predictive modeling can proceed more efficiently with reduced computational burden.
Solution Approach 2:
The patent calculates a selective set of statistical measures and features that are most relevant to the specific prediction task at hand, rather than computing all possible statistical descriptors. This partial action approach focuses computational resources on the most informative metrics, improving predictive capability while reducing unnecessary computational overhead.
3Measurement precision
If multiple statistical measures are used to characterize random patterns, then the comparison accuracy between different conditions improves, but the data processing complexity increases
Solution Approach 1:
The patent combines multiple statistical measures and features into integrated predictive models that assess random patterns comprehensively. By merging complementary statistical descriptors (such as combining spatial distribution metrics with structural features), the system achieves higher comparison accuracy while managing data processing complexity through unified analysis frameworks.
Data Source
AI summary
Disclosed herein is a system and a method for analyzing apparent random pathways, patterns, networks, or a series of events and characterizing these apparent random pathways, patterns, networks, or a series of events by constructal analysis. The resulting statistical values obtained can be used to compare the apparent random pathways, patterns, networks, or a series of events with other apparent random pathways, patterns, networks, or a series of events. The comparison can yield knowledge about the apparent random pathways, patterns, networks, or a series of events as well as the neighborhood or surroundings of the apparent random pathways, patterns, networks, or a series of events.