Endoscopic Image Feedback Control for Fluid Pressure and Flow
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Solution Overview
Problem
Current endoscopic systems rely on manual adjustments for fluid pressure and flow, which can lead to poor image quality, extravasation, and increased costs due to lack of automation and real-time image analysis, diverting attention from the procedure and requiring additional staff, and often result in suboptimal visualization settings.
Innovation Solution
An endoscopic system that includes an image analysis engine and control engine to analyze images in real-time, automatically adjusting fluid pressure and flow to optimize visualization, using machine learning algorithms to classify image characteristics and control system components such as medium management and light sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustments are made to fluid pressure and flow, then the procedure team can control the endoscopic system, but image quality deteriorates and attention is diverted from the procedure
Solution Approach 1:
The system automatically adjusts fluid pressure and flow based on real-time image analysis without requiring manual intervention. The control engine monitors image quality metrics and self-regulates medium delivery device parameters to maintain optimal visualization, allowing the procedure team to focus on the surgical procedure rather than system adjustments.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where the image analysis engine continuously evaluates image quality and provides real-time feedback to the control engine. This feedback drives automatic adjustments to fluid pressure and flow, creating a dynamic system that adapts to changing visualization conditions during the procedure.
2Reliability
If additional staff are assigned to adjust pressure and flow, then system control improves, but costs increase and sterile field contamination risk increases
Solution Approach 1:
The automated system eliminates the need for additional staff members to manually adjust pressure and flow. The control engine performs all system adjustments autonomously based on image analysis, reducing staffing requirements and minimizing the risk of sterile field contamination from non-sterile personnel.
Solution Approach 2:
The system replaces manual mechanical adjustments by personnel with an automated electronic control system. The control engine uses electronic signals to adjust medium delivery device parameters based on real-time image analysis, substituting human operation with automated mechanical and electronic control.
3Ease of operation
If fixed pressure and flow settings are used, then system operation is simplified, but image quality deteriorates under varying procedure conditions
Solution Approach 1:
The system transitions from static fixed settings to dynamic automatic adjustment. The control engine continuously modifies fluid pressure and flow based on real-time image quality assessment, allowing the system to adapt to varying procedural conditions while maintaining optimal visualization throughout the procedure.
Solution Approach 2:
The system automatically changes operational parameters (fluid pressure and flow) based on image quality requirements. The control engine adjusts these parameters in real-time according to the procedures being performed and the specific visualization needs at each moment, rather than using fixed predetermined settings.
Data Source
AI summary
Endoscopic image analysis, endoscopic procedure analysis, and/or component control systems, methods and techniques are disclosed that can analyze images of an endoscopic system and/or affect an endoscopic system to enhance operation, user and patient experience, and usability of image data and other case data.