SLAM-Guided Endoscope Feedback for Complete Organ Coverage
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Solution Overview
Problem
Endoscopic examinations are prone to variations in accuracy due to examiner proficiency, observation time, gastrointestinal cleanliness, and physiological phenomena, leading to potential overlook of pre-cancerous lesions or early cancers.
Innovation Solution
An electronic device that uses simultaneous localization and mapping (SLAM) to create a 3D model of the target organ, identifies examination and unexamined areas, and provides feedback information based on importance, observation time, and image quality to ensure comprehensive examination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If endoscopic examination is performed manually by examiners, then the examination can be conducted with existing equipment and procedures, but the accuracy and reliability of lesion detection vary due to examiner proficiency and observation time
Solution Approach 1:
The system provides real-time feedback by comparing actual endoscopic images with reference images from the database, notifying examiners when lesions are detected or when examination areas are missed. This feedback mechanism standardizes detection accuracy across different examiners and reduces variability in examination quality.
Solution Approach 2:
The system pre-processes endoscopic images in real-time, automatically detects potential lesions, and prepares comparison data before the examiner completes the examination. This preliminary automated analysis ensures that no potential lesions are overlooked due to human fatigue or inattention, thereby improving detection reliability.
2Reliability
If comprehensive examination of all gastrointestinal areas is performed, then lesion detection rate increases, but examination time and complexity increase
Solution Approach 1:
The system continuously monitors examination progress and provides real-time feedback on which areas have been examined and which remain unexamined. This enables examiners to efficiently complete comprehensive examinations by focusing attention on unexamined areas, reducing overall examination time while maintaining high detection rates.
Solution Approach 2:
The system pre-identifies high-risk areas based on patient history and preliminary imaging, prioritizing these regions for detailed examination. This preliminary prioritization allows examiners to allocate time efficiently, ensuring thorough examination of critical areas while reducing time spent on low-risk regions.
3Reliability
If manual examination procedures are used, then equipment complexity remains low, but examination quality varies due to human factors
Solution Approach 1:
The system performs automated image analysis, lesion detection, and examination quality assessment without requiring additional manual intervention from examiners. The AI algorithms automatically process images, compare them with reference data, and generate reports, thereby standardizing examination quality while adding minimal operational complexity.
Solution Approach 2:
The system replaces manual visual inspection and subjective judgment with automated computer-based image analysis and AI-driven lesion detection. This substitution of mechanical human examination with automated electronic analysis improves consistency and reliability while managing system complexity through software-based solutions.
Data Source
AI summary
The method for performing endoscopic examination according to an exemplary embodiment of the present invention may include the steps of obtaining an endoscopic image and a 3D model of a target organ; identifying location information of an endoscope on the 3D model in real time based on simultaneous localization and mapping (SLAM); classifying a plurality of areas constituting the 3D model into examination areas and unexamined areas by using the endoscopic image and the location information; and providing feedback information based on the examination areas and unexamined areas.