Multi-Position Endoscopic Analysis for Faster Lesion Screening
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Solution Overview
Problem
Current endoscopic screening systems for gastric cancer face challenges due to time constraints and lack of standardized methods for detecting precancerous conditions like intestinal metaplasia, which are difficult to discern visually, and there is a need for improved image analysis techniques and user-friendly interfaces for gastric cancer diagnosis.
Innovation Solution
An electronic device and method for processing endoscopic images that analyze images from multiple body positions, identify landmarks, and provide comprehensive information about lesions through multiple operating modes, using processors to obtain detection information and display visual indicators on a user-friendly interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If endoscopic screening is expanded to detect more patients, then gastric cancer detection coverage is improved, but time constraints and resource scarcity worsen
Solution Approach 1:
The patent replaces manual visual inspection by endoscopists with an AI-based automated detection system. The machine learning model processes endoscopic images automatically to identify precancerous conditions, eliminating the time-consuming manual analysis process while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables self-service diagnosis by automatically detecting and marking precancerous lesions in real-time during endoscopy. The AI model independently analyzes images without requiring continuous expert intervention, allowing the endoscope to 'diagnose itself' and reducing dependency on scarce qualified personnel.
2Ease of operation
If visual inspection methods are used for detecting precancerous conditions, then simplicity is maintained, but detection accuracy worsens
Solution Approach 1:
The patent substitutes human visual inspection with an AI-based image analysis system. The machine learning model processes endoscopic images to detect precancerous conditions with higher accuracy than manual inspection, while the system integrates seamlessly into the existing endoscopy workflow to maintain operational simplicity.
Solution Approach 2:
The AI model acts as an intermediary between the endoscopic images and the diagnosis. It processes visual information automatically, bridging the gap between simple image capture and accurate precancerous condition detection, thereby improving accuracy without complicating the overall system.
3Reliability
If standardized diagnostic systems are implemented for precancerous conditions, then detection reliability is improved, but system complexity worsens
Solution Approach 1:
The patent creates a universal AI-based diagnostic system that can detect multiple types of precancerous conditions (intestinal metaplasia, atrophic gastritis, dysplasia) using a single integrated machine learning model. This multi-functional approach improves diagnostic reliability across different conditions while avoiding the complexity of separate specialized systems for each condition.
Solution Approach 2:
The system standardizes diagnostic criteria by transforming subjective visual assessments into objective quantitative parameters. The AI model uses defined image analysis parameters and classification thresholds to ensure consistent, reproducible diagnoses across different endoscopists and patients, thereby improving reliability without requiring complex procedural standardization.
4Speed
If AI-based automated detection is implemented, then detection speed is improved, but technology integration complexity worsens
Solution Approach 1:
The patent replaces manual visual analysis with automated AI-based detection, dramatically increasing detection speed. The machine learning model processes endoscopic images in real-time, providing instantaneous feedback on precancerous conditions without requiring time-consuming manual review by endoscopists.
Solution Approach 2:
The AI system performs self-service image analysis automatically during endoscopy, eliminating the need for post-processing review. The model independently detects, localizes, and characterizes precancerous lesions in real-time, providing rapid diagnostic feedback without adding complex workflow steps or requiring additional specialized equipment.
Data Source
AI summary
According to one embodiment of the present disclosure, an electronic device for processing an endoscopic image may comprise a memory configured to store instructions and at least one processor electronically connected to the memory and configured to execute at least a portion of the instructions, wherein the at least one processor obtains an indicator associated with at least one lesion by operating in at least one of a plurality of operation modes.