Endoscope Suction Control via Image Recognition
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Solution Overview
Problem
Existing endoscope suction control systems struggle to automatically adjust suction based on real-time conditions, such as distance and adhesions, leading to suboptimal suction control and potential tissue damage.
Innovation Solution
A control device that acquires endoscope images to determine whether suction is needed and adjusts suction based on image recognition of distance, adhesions, and other conditions, allowing for automatic and adaptive suction control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automatic suction control is implemented without real-time image recognition, then device complexity is reduced, but suction control precision deteriorates leading to suboptimal suction and potential tissue damage
Solution Approach 1:
The system continuously captures endoscope images, recognizes tissue distance and adhesion states in real-time, and feeds this information back to automatically adjust suction intensity. This closed-loop feedback mechanism enables precise suction control that adapts to changing tissue conditions, resolving the contradiction between control precision and device complexity by integrating intelligent image processing into the control system.
Solution Approach 2:
The patent replaces manual mechanical suction control with an automated system that uses image recognition algorithms to detect tissue conditions and control suction intensity. This substitution of mechanical/manual control with intelligent automated control improves precision while managing complexity through software-based solutions.
2Productivity
If suction intensity is increased to improve productivity, then inspection efficiency is improved, but tissue damage risk increases
Solution Approach 1:
The suction intensity is dynamically adjusted based on real-time image recognition of tissue distance and adhesion states. The system automatically increases suction when tissue is distant and decreases suction when tissue is close or adhered, enabling high productivity during safe conditions while preventing tissue damage through adaptive intensity modulation.
Solution Approach 2:
The system changes the suction parameter (intensity) based on recognized tissue conditions. By monitoring distance and adhesion state and adjusting suction intensity accordingly, the system optimizes inspection efficiency while maintaining tissue safety, resolving the contradiction between productivity and harm prevention.
3Reliability
If manual suction control is used to simplify operation, then ease of operation is improved, but reliability deteriorates due to inability to respond to real-time conditions
Solution Approach 1:
The system performs self-service by automatically monitoring tissue conditions through image recognition and adjusting suction intensity without operator intervention. This autonomous control improves reliability by continuously adapting to real-time tissue states, while the simplified interface maintains ease of operation.
Solution Approach 2:
The automated feedback loop continuously monitors tissue distance and adhesion through endoscope images and adjusts suction accordingly, ensuring reliable adaptation to changing conditions. This eliminates the need for complex manual adjustments while maintaining high reliability through intelligent automated control.
Data Source
AI summary
A control device includes a processor. The processor acquires an endoscope image that is an image captured by an endoscope; determines whether suction is needed, based on the endoscope image; when the suction is determined to be needed, implements control to carry out the suction; determines a state of a subject based on a change in the endoscope image when the suction is performed; and performs control regarding suction based on the state of the subject.