Epithelial Tissue Structure Analysis via AI Image Processing
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Solution Overview
Problem
Current methods for analyzing and characterizing epithelial tissue structure are invasive or require extensive manual processing, limiting their ability for non-invasive or minimally invasive assessment.
Innovation Solution
The use of digital image processing and artificial intelligence algorithms to analyze confocal images of epithelial tissue, allowing for automated identification and characterization of epidermal tissue structure without the need for invasive procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional biopsy and manual histopathology methods are used to analyze epithelial tissue structure, then detailed tissue characterization can be achieved, but the procedures are invasive and require extensive manual processing
Solution Approach 1:
The patent replaces mechanical biopsy procedures and manual histopathology processing with optical imaging systems (confocal microscopy, OCT, multiphoton microscopy) that use light to visualize and characterize tissue structure non-invasively. This substitution eliminates the need for physical tissue removal and manual sectioning while maintaining diagnostic capability through automated image analysis algorithms
Solution Approach 2:
The patent creates optical copies (images) of the tissue structure using various imaging modalities. These optical replicas allow for detailed analysis without requiring physical copies obtained through biopsy. The imaging systems capture three-dimensional structural information that can be analyzed computationally, replacing the need for physical tissue sections
2Productivity
If automated image processing and AI algorithms are implemented to analyze confocal images, then productivity and efficiency are improved, but device complexity increases
Solution Approach 1:
The patent implements a multi-functional automated analysis system that performs multiple tasks: image acquisition from various modalities, preprocessing, feature extraction, classification, and quantitative analysis. This universal system handles different imaging types (confocal, OCT, multiphoton) and tissue structures using integrated AI algorithms, reducing the need for separate specialized tools while maintaining high productivity
Solution Approach 2:
The patent employs self-service mechanisms through automated image processing pipelines where the system performs its own analysis without extensive manual intervention. AI algorithms automatically segment tissue structures, identify features, and generate diagnostic information, allowing the system to serve itself in the analysis process and significantly improving productivity
Data Source
AI summary
Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and/or to specify the coordinates/locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.