Coordinate-Space Data Processing for Patient Responder Stratification
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Solution Overview
Problem
Existing techniques for patient stratification based on immune cell characteristics, such as those described in Non-Patent Document 1, face challenges in visually identifying stratification factors, especially when multiple factors are combined, making it difficult to accurately classify patients into responders and non-responders to medical treatments.
Innovation Solution
A data processing apparatus and method that utilizes a reinforcement learning convolutional neural network to modulate and plot patient data onto a coordinate space, allowing for the generation of high-precision discrimination lines to classify patients into responders and non-responders by formulating equations with selected factors and modulation methods, facilitating easy identification of stratification factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If visual confirmation methods are used to identify stratification factors, then the process is simple to understand, but the precision and efficiency of identifying factors deteriorate due to complicated visual confirmation work
Solution Approach 1:
The patent replaces manual visual confirmation with an automated image processing system that uses computer algorithms to analyze and identify stratification factors. The system automatically processes medical images and patient data to generate stratification results, eliminating the need for manual visual inspection while improving both precision and efficiency.
Solution Approach 2:
The patent introduces an image processing apparatus as an intermediary between raw medical data and stratification conclusions. This apparatus processes images through multiple stages (region of interest extraction, feature detection, analysis) to produce accurate stratification factors, serving as a bridge that maintains simplicity while enhancing precision.
2Measurement precision
If multiple factors are combined for patient stratification, then the accuracy of classification improves, but the difficulty of visually locating the combination from visualized images increases
Solution Approach 1:
The patent segments the complex analysis into distinct processing stages: first extracting regions of interest from images, then detecting specific features within those regions, and finally analyzing the combined data to identify stratification factors. This segmentation makes the complex multi-factor analysis manageable and traceable, allowing researchers to locate and understand each factor's contribution.
Solution Approach 2:
The patent transforms the visualization from a simple 2D image display to a multi-dimensional analysis space that includes image regions, extracted features, and their relationships. By adding these analytical dimensions, the system can clearly present complex factor combinations in an organized manner that is easy to detect and measure.
3Ease of manufacture
If manual visual analysis is used, then the method is easy to implement, but the productivity and efficiency of data analysis deteriorates due to complicated visual confirmation work
Solution Approach 1:
The patent implements a self-service system where the image processing apparatus automatically performs all analysis tasks without requiring manual intervention. The system self-manages image processing, feature extraction, and stratification factor identification, dramatically improving productivity while maintaining ease of implementation through automated workflows.
Solution Approach 2:
The patent performs preliminary processing of images and data before the main analysis stage. Regions of interest are pre-identified, features are pre-extracted, and data is pre-organized into structured formats. This preliminary action reduces the complexity of subsequent analysis and maintains ease of implementation while significantly boosting overall productivity.
Data Source
AI summary
A data processing apparatus includes: a storage section storing an object-to-be-analyzed data group having factors and an objective variable per object to be analyzed; a first modulation section modulating a first factor and outputting a first modulation result per object to be analyzed; a second modulation section modulating a second factor and outputting a second modulation result per object to be analyzed; and a generation section that assigns, per object to be analyzed, a coordinate point representing the first modulation result from the first modulation section and the second modulation result from the second modulation section to a coordinate space specified by a first axis corresponding to the first factor and a second axis corresponding to the second factor, and that generates first image data obtained by assigning information associated with the objective variable of the object to be analyzed corresponding to the coordinate point to the coordinate point.