Endoscope Camera Speed Determination Using Machine Learning
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Solution Overview
Problem
Medical practitioners face challenges in determining the optimal moving speed of an endoscope camera within a gastrointestinal tract, particularly in large intestines, due to reliance on experience and lack of real-time speed feedback.
Innovation Solution
A system and method utilizing a processor and machine learning algorithms to establish a speed-determining model based on training sets of gastrointestinal images captured at various speeds, allowing for real-time determination and notification of the endoscope camera's speed, with integration of a speed-determining model and a detecting model for abnormal condition detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical practitioners operate endoscope based on experience and expertise, then operation flexibility is maintained, but moving speed control precision deteriorates
Solution Approach 1:
The system pre-trains a machine learning model using training sets of gastrointestinal images captured at various known speeds. This preliminary training phase allows the model to learn the relationship between image features and camera speed before actual endoscopy procedures, enabling accurate speed determination without adding complexity to the real-time operation.
Solution Approach 2:
The system implements a feedback mechanism where the trained model continuously analyzes captured images and provides real-time speed feedback to the operator. This closed-loop feedback allows practitioners to adjust their operating speed based on quantitative information, improving speed control precision while maintaining operational flexibility.
2Measurement precision
If machine learning model is trained with comprehensive training sets, then speed determination accuracy is improved, but training time and computational resources increase
Solution Approach 1:
The system performs model training in advance using comprehensive training sets captured at multiple preset speeds. By completing this time-consuming training phase beforehand, the system avoids delaying actual medical procedures while still benefiting from accurate speed determination capabilities.
Solution Approach 2:
The system uses a dynamic training approach where the model is initially trained with diverse training sets at various speeds, then fine-tuned using validation sets. This dynamic training process optimizes the model's adaptability to different operating conditions while managing training time efficiently.
3Reliability
If real-time speed monitoring is implemented, then operating safety is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by having the machine learning model automatically analyze captured images and determine camera speed without requiring additional external monitoring equipment or complex intervention systems. The model processes images independently and provides speed information that can be directly used for safety monitoring.
Solution Approach 2:
The system replaces complex mechanical speed sensing equipment with an image-based machine learning approach. Instead of using additional sensors or mechanical measurement devices, the system uses software-based analysis of standard endoscopic images to infer speed, thereby maintaining reliability while minimizing added hardware complexity.
Data Source
AI summary
A method includes steps of: based on training sets of gastrointestinal images, using a predetermined machine learning algorithm to obtain a preliminary model; feeding preliminary validation sets of gastrointestinal images into the preliminary model to obtain estimation results; based on the estimation results, selecting, from the preliminary validation sets of gastrointestinal images, a series of successive images as a selected validation set of gastrointestinal images; based on the selected validation set of gastrointestinal images, tuning parameters of the preliminary model to result in a speed-determining model for determining a moving speed of an endoscope camera.