Endoscope Processor Automatic Illumination Mode Switching
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Solution Overview
Problem
Endoscope operators face a high burden in manually switching between illumination modes during examinations, as existing systems require manual intervention based on the type of observation site or detection target, which can be time-consuming and inefficient.
Innovation Solution
An endoscope apparatus that automatically switches between observation modes (normal-light and special-light modes) based on continuous detection of a target from images captured, using a processor to determine when to switch between modes to ensure detailed observation without operator intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual switching between illumination modes is performed based on observation site type or detection target, then the operator can control the observation mode, but the operator burden increases and examination efficiency decreases
Solution Approach 1:
The endoscope system automatically determines the observation site type using image analysis and machine learning, and autonomously switches between illumination modes without requiring manual operator intervention. The system serves itself by processing images and making mode switching decisions based on detected features such as lesion characteristics, thereby eliminating the need for operators to manually control mode transitions
Solution Approach 2:
The system continuously analyzes images captured during examination, detects features such as lesion type and characteristics, and uses this feedback information to automatically adjust and switch illumination modes. The feedback loop enables real-time adaptation of observation parameters based on the actual visual data, optimizing the examination process dynamically
2Ease of operation
If automatic mode switching is implemented based on image analysis, then operator burden is reduced, but system complexity increases
Solution Approach 1:
The endoscope system integrates multiple functions including image capture, machine learning-based lesion detection, feature analysis, and automatic illumination mode switching within a single integrated platform. The machine learning model serves multiple purposes by detecting various lesion types and characteristics simultaneously, reducing the need for separate dedicated systems for each function
Solution Approach 2:
A machine learning model acts as an intermediary between the image capture system and the illumination control system. The model processes visual data and translates it into control decisions for mode switching, serving as an intelligent mediator that bridges perception and action without requiring direct complex coupling between imaging and control subsystems
3Measurement precision
If special-light observation mode is used for detailed lesion observation, then observation accuracy improves, but the system requires manual mode switching which increases operation time
Solution Approach 1:
The machine learning model continuously analyzes images in real-time to preliminarily identify potential lesions and their characteristics before the operator completes the examination. By proactively detecting and characterizing lesions, the system prepares the optimal illumination mode in advance, enabling seamless transitions to special-light modes when lesions are detected without requiring time-consuming manual assessment and mode switching
Data Source
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AI summary
Provided are an endoscope apparatus, an endoscope processor, and a method for operating the endoscope apparatus in which illumination light (observation mode) is automatically switched in response to detection of a detection target from an image to reduce the burden of an operator's switching operation. An endoscope sequentially captures images of a subject in a first observation mode in which first illumination light (white light) is used. It is determined whether a detection target is continuously detected from the sequentially captured images. If it is determined that the detection target is continuously detected, the observation mode is automatically switched to a second observation mode in which second illumination light (special light) is emitted. In response to an elapse of a certain period of time after the observation mode is switched to the second observation mode, the observation mode is automatically switched from the second observation mode to the first observation mode.