Endoscope Lesion Matching Using Image and Position Comparison
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Solution Overview
Problem
Conventional endoscopes lack effective methods for accurately identifying and tracking lesions across multiple examinations, making it difficult to determine if observed lesions are identical and complicating follow-up observations or treatments.
Innovation Solution
An endoscope control device and system that includes processors to acquire and analyze endoscope and image information at multiple timings, performing lesion identification determinations based on endoscope and image information to generate comparison information for lesion identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional endoscope systems record lesion information manually, then the system structure remains simple, but the accuracy of lesion identification across multiple examinations deteriorates
Solution Approach 1:
The patent replaces manual recording methods with automated image processing and AI-based lesion identification systems. The processor automatically extracts lesion features from endoscopic images, compares them across examinations, and generates identification results without manual intervention, thereby improving accuracy while managing complexity through automation.
Solution Approach 2:
The patent creates digital copies of lesion information from endoscopic images by extracting and storing lesion features, positions, and characteristics. These digital representations are then used for comparison across multiple examinations, enabling accurate lesion tracking without requiring physical re-examination or manual documentation.
2Reliability
If multiple examination data are collected and compared manually, then comprehensive lesion tracking is possible, but the time and labor required increase significantly
Solution Approach 1:
The patent implements self-service through automated systems that perform lesion identification, data comparison, and result generation without requiring physician intervention. The processor automatically retrieves stored examination data, compares lesion features, and produces identification results, enabling reliable lesion tracking while minimizing time investment from medical professionals.
Solution Approach 2:
The patent establishes continuous automated processing of examination data by maintaining databases of lesion information from multiple examinations and enabling on-demand comparison. This continuous availability of processed data allows reliable lesion tracking to be performed quickly when needed, rather than requiring time-consuming manual review of all historical data.
3Measurement precision
If detailed lesion information is recorded and stored, then accurate comparison between examinations becomes possible, but the data management complexity and storage requirements increase
Solution Approach 1:
The patent extracts only the essential lesion features and characteristics from complete endoscopic images and stores them in structured format. By extracting key parameters such as lesion position, size, shape, and visual characteristics rather than storing entire images or all possible data, the system achieves accurate lesion comparison while minimizing data storage requirements and management complexity.
Data Source
AI summary
An endoscope control device includes a connection interface, and one or more processors including hardware that are configured to acquire, via the connection interface, endoscope information and image information of an observation target region at a plurality of timings at which an endoscope is positioned in the observation target region, and perform lesion identification determinations each determining whether lesions observed at the plurality of timings are identical to each other, thereby generating comparison information including one or more results derived from the lesion identification determinations. The lesion identification determinations include a first lesion identification determination based on the endoscope information, a second lesion identification determination based on the image information, and a third lesion identification determination based on results of the first lesion identification determination and the second lesion identification determination.