Colonoscope Withdrawal Speed Monitoring via Image Hashing
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Solution Overview
Problem
Colonoscope withdrawal speed during colonoscopy procedures lacks effective supervision and monitoring, which can lead to suboptimal detection rates of polyps and adenomas, as current clinical practices often fail to adhere to recommended 6-10 minute withdrawal times.
Innovation Solution
A method and device that utilize real-time video acquisition, image processing to obtain Hash fingerprints, calculate Hamming distances, and convert these into stability coefficients to monitor and feedback the withdrawal speed, providing alerts for standard, sub-standard, and low-quality procedures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real-time video acquisition and image processing are implemented to monitor withdrawal speed, then monitoring capability is improved, but device complexity increases
Solution Approach 1:
The patent replaces direct mechanical measurement of withdrawal speed with an optical-based image processing system. By capturing video frames and analyzing changes in the colonoscope's visual appearance over time, the system infers withdrawal speed without requiring direct mechanical sensors or contact measurement, thus reducing hardware complexity while maintaining monitoring capability.
Solution Approach 2:
The patent introduces an intermediary processing layer between video acquisition and withdrawal speed determination. By using image processing algorithms to analyze frame differences and extract motion information, the system creates a computational mediator that translates visual data into quantitative speed measurements, simplifying the overall system architecture.
2Reliability
If real-time monitoring and feedback mechanisms are added, then withdrawal speed control is improved, but operation complexity increases
Solution Approach 1:
The patent implements a feedback mechanism that provides real-time information to the operator about the current withdrawal speed. By displaying processed video frames or quantitative speed data during the colonoscopy procedure, the system enables operators to adjust their withdrawal rate dynamically, improving reliability of guideline adherence without requiring complex manual control systems.
Solution Approach 2:
The system performs self-monitoring and self-evaluation of withdrawal speed automatically, eliminating the need for external monitoring devices or complex operator interventions. The automated image processing and analysis handle the measurement and feedback functions, reducing operational complexity while maintaining high reliability.
3Measurement precision
If image processing algorithms are applied to analyze texture and color changes, then measurement precision is improved, but processing time increases
Solution Approach 1:
The patent extracts only the essential visual features from the colonoscope images, such as texture patterns and color characteristics, for comparison purposes. By focusing analysis on these key features rather than processing every pixel in detail, the system achieves high measurement precision while reducing computational processing time and maintaining real-time capability.
Solution Approach 2:
The patent applies image processing algorithms selectively to specific frames or regions rather than continuously analyzing all video data. By processing only the necessary portions of the video stream, the system balances measurement precision requirements with processing time constraints, maintaining real-time monitoring capability.
Data Source
AI summary
A method for monitoring a colonoscope withdrawal speed, the method including: 1) acquiring, a real-time video of colonoscopy, decoding the video into images, cropping the images, and zooming-out the images cropped where texture information of the images is retained; 2) converting the images including the texture information to grayscale images; 3) obtaining Hash fingerprints of the images; 4) calculating a Hamming distance between the images; 5) comparing the Hash fingerprints of a current colonoscopy image with n previous colonoscopy images, to obtain an overlapping rate of the current colonoscopy image with any one of the n previous colonoscopy images; 6) calculating a weighted similarity of the images at a point in time t; 7) converting a weighted overlapping rate of the images at the point in time t into a stability coefficient; and 8) calculating a mean stability coefficient of colonoscopy images within a period of time 0-t.