Tissue Section Classification Using Multispectral Dye Dynamics
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Solution Overview
Problem
The identification of sections of bodily tissue for pathology tests is subjective and time-consuming, often relying on qualitative observations of dye substance transport, leading to excessive tissue sampling.
Innovation Solution
A classification model trained on labeled data using machine-learning techniques to analyze multispectral images of dye substance transport dynamics, determining tissue sections as biopsy candidates or non-candidates based on physical models describing dye dynamics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If qualitative observations of dye substance transport are used for tissue identification, then the method is simple to operate, but it is subjective and time-consuming leading to excessive tissue sampling
Solution Approach 1:
The patent replaces the mechanical/visual inspection method with an automated machine learning system. The classification model processes multispectral images and dye dynamics data automatically, eliminating the need for subjective visual observation while maintaining operational simplicity through automated decision-making.
Solution Approach 2:
The patent creates a digital representation (feature vector) of the tissue's dye dynamics behavior. This digital copy is then analyzed by the classification model to determine biopsy candidacy, replacing the need for direct visual inspection of tissue sections while maintaining the essential diagnostic information.
2Device complexity
If qualitative observations of dye substance transport are used for tissue identification, then the method requires minimal equipment, but it leads to excessive tissue sampling and increased cost
Solution Approach 1:
The patent transforms the analysis from qualitative visual parameters to quantitative parameters (feature vectors capturing dye dynamics). This parameter transformation enables automated classification that reduces tissue sampling by identifying only the most promising areas for biopsy, thereby reducing tissue loss while maintaining diagnostic accuracy.
Solution Approach 2:
The patent substitutes manual visual assessment with an automated classification system that processes quantitative data. This substitution enables more precise tissue selection, reducing the amount of tissue that needs to be sampled and removed, thus addressing the tissue loss problem while requiring only standard imaging equipment.
3Productivity
If machine learning classification is applied to automate tissue selection, then productivity and objectivity are improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex classification task into manageable components: data acquisition (multispectral imaging), feature extraction (creating feature vectors from dye dynamics), and classification (applying the trained model). This segmentation reduces the perceived complexity by breaking down the automated system into distinct functional modules that can be implemented and maintained more easily.
4Measurement precision
If quantitative analysis of dye dynamics is performed, then measurement precision and objectivity are improved, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary action by pre-training the classification model on labeled data before actual tissue analysis. This pre-training enables the system to make rapid, accurate classifications during real-time surgical procedures without requiring time-consuming analysis during the actual biopsy decision-making process.
Solution Approach 2:
The patent creates a compressed digital representation (feature vector) that captures the essential dye dynamics characteristics. This copying approach allows for rapid analysis of tissue sections without requiring time-consuming detailed examination, as the feature vectors can be processed quickly by the classification model to determine biopsy candidacy.
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
This approach automates tissue selection for pathology tests, reducing subjectivity and cost, and efficiently identifies sections requiring analysis, potentially minimizing tissue collection.
Implementation Method 1
generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue
Implementation Method 2
generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector
Data Source
AI summary
Embodiments are provided for identification of a section of bodily tissue as either a candidate or a non-candidate for pathology tests. In some embodiments, a system can include a processor that executes computer-executable components stored in memory. The computer-executable components can include a feature composition component that generates a feature vector representing a physical model describing dye dynamics that determines a group of multispectral images of a section of bodily tissue. The computer-executable components also can include a classification component that generates a classification attribute for the section of bodily tissue by applying a classification model to the feature vector. The classification attribute designates the section of bodily tissue as one of biopsy-candidate or non-biopsy-candidate.