Endoscopic Turbidity Analysis With Closed-Loop Fluid Control
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Solution Overview
Problem
Endoscopic imaging environments are impaired by turbidity caused by blood, urine, or other particles, limiting the view during medical interventions.
Innovation Solution
An endoscopic system with an imager and processor that determines image metrics, analyzes changes over time, and adjusts fluid delivery based on turbidity metrics to enhance image clarity and manage fluid flow, using machine learning algorithms to classify particles and blood content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluid is circulated to manage turbidity, then image clarity is improved, but device complexity increases
Solution Approach 1:
The system continuously monitors image metrics (entropy, clarity scores) and uses this feedback to automatically adjust fluid delivery parameters, creating a closed-loop control system that maintains optimal image clarity without requiring complex manual intervention
Solution Approach 2:
The system performs self-adjustment by automatically analyzing image quality and modifying fluid delivery based on detected turbidity levels, enabling the system to maintain optimal performance without external control
2Measurement precision
If machine learning algorithms are used to classify particles, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system replaces manual visual inspection and mechanical particle analysis with machine learning algorithms that automatically classify particles based on image features, significantly improving measurement precision while the algorithms handle the computational complexity
3Measurement precision
If image metrics are continuously monitored over time, then turbidity measurement precision is improved, but loss of time increases
Solution Approach 1:
The system analyzes a subset of image frames and selects key representative frames for detailed metric calculation, rather than processing every single frame, thereby maintaining measurement precision while reducing overall processing time
Solution Approach 2:
The system performs preliminary filtering and pre-processing of image frames to identify those most relevant for turbidity analysis, preparing data in advance to accelerate the actual measurement process
Data Source
AI summary
An endoscopic system includes an endoscopic imager configured to capture images of a target site within a living body and a processor configured to determine one or more image metrics for each one of a plurality of image frames captured over a time span, analyze changes in the image metrics over the time span, and determine a turbidity metric for the target site based on the analyzed changes in the image metrics.