Endoscope System Grasp Traction Assistance
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Solution Overview
Problem
Current endoscope systems lack effective assistance in grasping and traction operations during surgical procedures, relying heavily on surgeon experience and varying in manipulation quality due to subjective evaluation criteria.
Innovation Solution
An endoscope system with a control device that processes endoscopic images to derive and display grasp assistance information and traction assistance information, using machine learning models to evaluate tissue features and provide objective guidance for optimal grasping and traction operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If endoscope systems rely on surgeon experience for manipulation operations, then the system structure remains simple, but the manipulation quality varies and is subjective
Solution Approach 1:
The patent replaces subjective human judgment with an automated image processing system that uses machine learning models to objectively evaluate tissue characteristics and provide manipulation guidance. The control device processes endoscopic images through neural networks to derive quantitative assessment data, substituting the mechanical/human-based evaluation system with an automated computational system.
Solution Approach 2:
The system enables self-service by automatically analyzing endoscopic images and generating manipulation guidance information without requiring continuous human intervention for assessment. The control device autonomously processes images, evaluates tissue states, and provides real-time guidance for grasping and traction operations, allowing the system to serve itself in the analysis and guidance generation.
2Reliability
If endoscope systems provide objective manipulation assistance, then manipulation stability improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models with extensive surgical data and tissue characteristic information before actual surgical procedures. The neural networks are trained in advance to recognize tissue patterns and evaluate manipulation appropriateness, so that during real-time surgery, the system can quickly derive guidance information without performing complex analysis from scratch.
Solution Approach 2:
The system manages processing time by dynamically adjusting evaluation parameters based on surgical context. The control device selects and processes only the most relevant tissue characteristics and image features for each specific manipulation task, changing the scope and depth of parameter analysis according to the immediate surgical needs rather than consistently performing comprehensive analysis.
3Measurement precision
If endoscope systems use machine learning to evaluate tissue features, then objective guidance is provided, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer in the form of pre-trained machine learning models that mediate between raw endoscopic images and surgical decision-making. These models serve as intermediaries that automatically extract meaningful tissue feature information and transformation characteristics, simplifying the overall system architecture by providing a black-box solution for complex pattern recognition without requiring explicit programming of all evaluation logic.
Data Source
AI summary
Provided is an endoscope system including an endoscope that images living tissue to be treated by at least one instrument; and a control device that includes a processor configured to derive at least one of the following information: grasp assistance information on a grasping operation to grasp the living tissue by the at least one instrument, and traction assistance information on a traction operation for traction of the living tissue by the at least one instrument, based on an endoscopic image acquired by the endoscope.