Endoscopic Image Grouping for Rapid Bleeding Source Identification
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Solution Overview
Problem
In endoscopy, the time required for image interpretation increases with the number of captured images, as doctors need to observe and analyze multiple images to identify lesions such as bleeding areas, leading to inefficiencies in diagnosis and reporting.
Innovation Solution
A medical assistance system that classifies endoscopic images taken inside a subject's body into distinct groups based on predetermined criteria, such as temporal proximity, shooting position, and image features, allowing for efficient display and identification of bleeding images and their potential sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple endoscopic images are captured to ensure complete examination coverage, then diagnostic reliability is improved, but the time required for image interpretation increases
Solution Approach 1:
The patent segments the large set of captured endoscopic images into multiple groups based on temporal proximity and shooting position. By dividing the images into manageable groups (e.g., first group, second group, third group), the system enables doctors to review organized subsets rather than overwhelming them with all images at once, thus reducing interpretation time while maintaining diagnostic reliability through systematic coverage of all image groups.
2Loss of information
If all captured images are displayed sequentially for review, then complete image coverage is achieved, but diagnostic efficiency decreases
Solution Approach 1:
The system performs preliminary classification of images into groups based on temporal proximity and shooting position before the doctor reviews them. This preliminary organization action ensures that when the doctor accesses the images, they are already sorted in a logical sequence, allowing complete image coverage to be achieved without requiring the doctor to manually review every single image in random order, thereby improving diagnostic efficiency.
Data Source
AI summary
A processing unit classifies multiple images taken inside a subject's body into multiple groups based on a certain criterion. The processing unit distinguishably displays images in the multiple groups.