Automated Endoscopic Control Using Neural Network Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual control of medical equipment during procedures like endoscopies is time-consuming and prone to errors, requiring automated systems to streamline operations and improve efficiency.
Innovation Solution
An automated endoscopic device control system utilizing neural networks and machine learning to detect conditions and automatically control devices such as cameras, light sources, and insufflators, by processing image and video data to determine necessary actions and send control signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual controls are used to adjust display settings and activate tools, then operators can control medical equipment, but the procedure becomes time-consuming and requires multiple operators
Solution Approach 1:
The system enables self-service automation where the endoscopic device automatically adjusts display settings, activates tools, and performs operations based on real-time image analysis and machine learning algorithms, eliminating the need for manual operator intervention for these tasks
Solution Approach 2:
The patent replaces manual mechanical controls with an automated control system that uses machine learning models and image processing to automatically determine and execute appropriate actions, substituting human operators with an intelligent automated system
2Reliability
If multiple operators are involved in controlling medical equipment, then comprehensive control is achieved, but coordination complexity and potential for error increase
Solution Approach 1:
The automated control system performs multiple functions including image analysis, decision-making, device control, and coordination of multiple endoscopic devices through a single integrated system, replacing the need for multiple specialized operators
3Productivity
If automated control systems are implemented, then procedure time is reduced and accuracy is enhanced, but system complexity increases
Solution Approach 1:
The system uses machine learning models and image processing algorithms as intermediaries between the endoscopic camera and the control devices, translating visual information into automated control actions without requiring direct human intervention
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for automated endoscopic device control systems. In one embodiment, an example endoscopic device control system may include memory that stores computer-executable instructions, and at least one processor configured to access the memory and execute the computer-executable instructions to determine a first image from an endoscopic imaging system comprising a camera and a scope, determine, using the first image, that a first condition is present, determine a first response action to implement using a first endoscopic device, and automatically cause the first endoscopic device to implement the first response action.