Automated Pathology Analysis for Gene Mutation Prediction
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Solution Overview
Problem
The process of deriving medical information from pathological slide images is costly and time-consuming due to subjective judgment by pathologists, which can lead to inaccuracies in clinical conclusions, especially in determining gene mutations and biomarkers crucial for cancer diagnosis and treatment.
Innovation Solution
A computing device and method that obtain feature information from pathological slide images using machine learning models to generate medical information, including gene mutations and biomarkers, thereby reducing reliance on human judgment and enhancing accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pathologists manually analyze pathological slide images to determine medical information, then the process can be performed with existing technology, but the process is costly and time-consuming with subjective judgment leading to inaccuracies
Solution Approach 1:
The patent replaces the manual mechanical analysis system (pathologists visually examining slides) with an automated computational system that uses machine learning models to process digital pathological slide images. The system extracts feature information through image processing algorithms and generates medical information automatically, eliminating the need for human pathologists to manually analyze each slide, thereby reducing time and cost while improving consistency and accuracy.
Solution Approach 2:
The patent creates a digital copy of the pathological slide image and analyzes this copy using machine learning models rather than physically examining the original slide. The system extracts feature information from the digital image data and generates medical information predictions, effectively copying the analysis function from human pathologists to computational algorithms that can process images rapidly and consistently.
2Reliability
If pathologists manually analyze pathological slide images, then the system is simple, but the process is costly and subject to human error
Solution Approach 1:
The patent replaces the simple manual examination process with a computational system that uses machine learning models trained on large datasets of pathological images. The system automatically extracts feature information through image processing and generates medical information predictions, improving reliability by eliminating human subjectivity and error while the complexity is managed through automated algorithms rather than requiring complex manual procedures.
3Productivity
If machine learning models are used to generate medical information, then speed and accuracy improve, but the system complexity increases
Solution Approach 1:
The patent segments the complex medical image analysis task into distinct functional components: image input, feature extraction through machine learning models, and medical information generation. By dividing the overall system into these manageable segments, each handled by specialized algorithms or models, the system achieves high productivity through automated processing while managing complexity through modular architecture rather than requiring a single monolithic complex system.
Data Source
AI summary
Provided is a computing device including at least one memory, and at least one processor configured to obtain feature information corresponding to a pathological slide image, generate medical information associated with the pathological slide image based on the feature information, and output at least one of the medical information and additional information based on the medical information.