Endoscope Operation Data Analysis for Key Site Inspection Guidance
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Solution Overview
Problem
Medical endoscopy procedures often require complex manual operations, leading to inconsistencies in reaching key site locations, with inexperienced doctors potentially missing sites and causing discomfort to patients due to improper endoscope motion.
Innovation Solution
An information processing method and system that uses machine learning techniques to analyze endoscope operation data, providing real-time guidance to doctors by determining the current location, motion track, and next destination of the endoscope, ensuring all key sites are inspected efficiently and effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual operation of endoscope is performed by doctors, then operational flexibility is maintained, but consistency in reaching key site locations deteriorates
Solution Approach 1:
The system provides real-time feedback to doctors by displaying the current location of the endoscope, the motion track taken, and guidance on the next destination. This feedback loop enables doctors to adjust their manual operations to consistently reach key site locations while maintaining operational flexibility.
Solution Approach 2:
The patent replaces reliance on doctor's manual skill and experience with an automated computer vision system that uses image recognition and machine learning algorithms to determine endoscope location and guide operations, ensuring consistent accuracy across different operators.
2Productivity
If experienced doctors perform endoscopy independently, then operational efficiency is improved, but quality control consistency deteriorates
Solution Approach 1:
The system implements comprehensive quality control through real-time monitoring and feedback, automatically evaluating whether key sites have been reached and providing guidance. This ensures that all doctors, regardless of experience level, can achieve consistent quality standards while maintaining their operational efficiency.
Solution Approach 2:
The system performs self-evaluation of the endoscopy process by automatically analyzing images to determine if key sites have been reached, reducing reliance on subjective doctor assessment and ensuring objective, consistent quality control across all procedures.
3Measurement precision
If more comprehensive data collection is performed during endoscopy, then diagnostic accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the essential information needed for quality control and diagnosis by automatically identifying key site locations and determining whether they have been reached. This selective extraction of critical data maintains diagnostic accuracy while avoiding the complexity of processing all collected images manually.
Solution Approach 2:
The patent replaces complex manual data processing with automated computer vision algorithms and machine learning models that efficiently analyze images, identify key anatomical landmarks, and determine procedure completion, significantly reducing processing complexity while maintaining or improving diagnostic accuracy.
Data Source
AI summary
Provided are an information processing method, an electronic device, and a computer storage medium. The method comprises: receiving first information which is associated with an operation behavior of a medical testing device, wherein the first information is associated with data collection performed by the medical testing device during operation; and outputting at least part of the first information. By using the method, quality control can be performed on operation behaviors associated with examination, and the quality of a result obtained by an operation behavior, deviation from a recommended operation, a suggested modification direction and any possible statistical information can be presented, thereby a doctor can be helped in improving operations performed on a medical testing device.