Tissue Section Analysis Score for Pathological Diagnosis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional pathological diagnosis relies heavily on subjective judgments by pathologists, leading to variations in diagnosis results, as existing methods do not comprehensively evaluate the relationship among regions, structures, and cell types in tissue sections, limiting accuracy in prognosis and treatment planning.
Innovation Solution
An information provision method and device that obtain and analyze digital bright-field and fluorescent images of tissue sections, combining information on regions, structures, and cell types to create an analysis score, which is presented as objective support information for improved diagnostic accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If digital analysis technology is used to eliminate subjective variation, then objectivity is improved, but comprehensive judgment capability deteriorates
Solution Approach 1:
The comprehensive judgment capability is segmented into three independent analysis modules: region analysis (tumor vs. normal tissue), structure analysis (blood vessels, lymphatic vessels, nerves), and cell type analysis (immune cells, tumor cells, stromal cells). Each module independently evaluates specific aspects and generates separate scores, which are then integrated to achieve comprehensive judgment while maintaining objectivity through digital analysis.
Solution Approach 2:
The patent merges multiple independent analysis results (region scores, structure scores, cell type scores) into a unified comprehensive evaluation system. By combining these segmented analyses with their spatial relationships, the system achieves both objective digital analysis and comprehensive pathological judgment capability.
2Measurement precision
If multiple kinds of information are combined and scored, then accuracy is improved, but device complexity increases
Solution Approach 1:
The complex analysis system is segmented into three independent modules (region analysis, structure analysis, cell type analysis), each handling specific types of information. This segmentation reduces the complexity of processing multiple information types by dividing them into manageable, specialized components that can be independently optimized and maintained.
Solution Approach 2:
The patent transforms multiple qualitative pathological parameters into quantitative scores through standardized algorithms. By converting region characteristics, structure densities, and cell concentrations into numerical values with defined weightings, the system manages complexity through parameter standardization and mathematical integration rather than complex qualitative assessment.
Data Source
AI summary
There is provided an information provision method for providing support information for supporting a judgement based on information obtained from a tissue section. The method includes: obtaining a digital bright-field image of a tissue section stained to be observable in a bright field; creating an analysis score by obtaining, combining, and scoring multiple kinds of information on the bright-field image; and presenting the analysis score as the support information.