Rectal Effluent Image Analysis for Accurate Bowel Prep Assessment
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Solution Overview
Problem
Current colonoscopy preparation methods are inadequate, leading to increased healthcare costs and suboptimal patient outcomes due to inadequate bowel preparations, which are often not accurately assessed by patient descriptions of rectal effluent quality.
Innovation Solution
A system and method for evaluating bowel preparation using image analysis of rectal effluent, employing a reticle and neural networks to assess parameters such as color, turbidity, and viscosity, providing real-time feedback on bowel readiness for colonoscopy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If patient descriptions of rectal effluent quality are used to assess bowel preparation, then the assessment process is simple and quick, but the accuracy and reliability of the assessment deteriorates
Solution Approach 1:
The patent replaces the subjective mechanical assessment method (patient visual inspection and description) with an automated image analysis system using cameras and neural networks. The system captures images of rectal effluent and automatically analyzes parameters such as color, turbidity, and viscosity, substituting human judgment with objective computational analysis to improve measurement precision while maintaining ease of operation.
2Measurement precision
If image analysis with neural networks is used to assess bowel preparation, then the measurement precision improves to 99% accuracy, but the device complexity increases
Solution Approach 1:
The patent uses image capture to create a digital copy of the rectal effluent, which is then analyzed by neural networks. Instead of directly analyzing the physical sample with complex instrumentation, the system creates an optical replica (image) that can be processed computationally, simplifying the physical device while achieving high measurement precision through software-based analysis.
Solution Approach 2:
The system transforms the assessment from analyzing multiple complex physical parameters simultaneously to focusing on key visual parameters (color, turbidity, viscosity) that can be extracted from images. This parameter transformation allows the use of neural networks to achieve 99% accuracy while keeping the device relatively simple by concentrating on the most discriminative visual features.
3Productivity
If inadequate bowel preparations are not identified, then the colonoscopy procedure can proceed without delay, but the reliability of the procedure deteriorates due to missed lesions and repeat procedures
Solution Approach 1:
The patent implements preliminary assessment of bowel preparation quality using image analysis before the colonoscopy procedure begins. By evaluating rectal effluent characteristics in advance, the system identifies inadequate preparations that would compromise procedure reliability, allowing for corrective action (additional preparation or rescheduling) before the colonoscopy, thus preventing missed lesions and repeat procedures while maintaining efficient scheduling.
Solution Approach 2:
The system provides feedback on bowel preparation quality by analyzing images of rectal effluent and comparing them against optimal preparation criteria. This feedback mechanism enables real-time assessment and communication of preparation adequacy to patients and providers, allowing for adjustments before the procedure to ensure reliability without unnecessarily delaying scheduled colonoscopies.
Data Source
AI summary
Embodiments relate to devices, methods, and systems to assess the status of a bowel preparation in a subject. In an embodiment, the device, the methods, and the system assess the status of bowel preparation in a subject in real-time.