Peritoneal Dialysis Image Analysis for Complication Detection
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Solution Overview
Problem
Naked-eye visual inspection of peritoneal dialysis recovered solution is inefficient for detecting complications, such as peritonitis, in peritoneal dialysis, which requires a more effective method for monitoring patient health.
Innovation Solution
An image analysis method and apparatus that captures images of dialysis bags, performs edge detection, color correction, and comparison with disease warning ranges in a database to detect chrominance changes, sending prompt signals when warning ranges are approached, utilizing a color correction matrix and CIE Lab color space for accurate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If naked-eye visual inspection is used to check peritoneal dialysis recovered solution, then the method is simple and requires no special equipment, but the detection efficiency and accuracy for complications is insufficient
Solution Approach 1:
The patent replaces the mechanical visual inspection system with an electronic image analysis system. A mobile device captures images of the dialysis solution, and software algorithms automatically analyze color characteristics to detect complications like peritonitis. This substitution transforms a subjective visual task into an objective digital analysis, significantly improving detection accuracy while keeping the device complexity low by using commonly available mobile devices.
Solution Approach 2:
The patent creates a digital copy (image) of the dialysis solution instead of directly analyzing the physical solution. The image capture and subsequent digital processing allow for repeated analysis, archiving, and remote consultation without requiring multiple physical samples or complex laboratory equipment. This copying approach enables accurate complication detection while maintaining simplicity.
2Productivity
If naked-eye visual inspection is used, then no special equipment is needed, but the monitoring efficiency and timeliness for complication detection is poor
Solution Approach 1:
The patent enables patients to perform self-monitoring by capturing images of their own dialysis solution using their mobile devices. The automated image analysis algorithm processes the images locally or remotely, providing timely detection of complications without requiring physician intervention for each assessment. This self-service capability dramatically improves monitoring efficiency and timeliness while keeping the system simple to operate.
Solution Approach 2:
The system provides immediate feedback by analyzing the captured image and comparing color characteristics against reference ranges for normal and abnormal dialysis solutions. The rapid automated analysis delivers timely results that enable prompt medical intervention when complications are detected, significantly improving monitoring efficiency compared to delayed manual inspection.
3Measurement precision
If automated image analysis with color correction is implemented, then detection accuracy improves, but processing complexity and computational requirements increase
Solution Approach 1:
The patent transforms the raw color data from images into standardized chrominance parameters through color correction matrices and conversion to color spaces like CIE Lab. This parameter transformation compensates for variations in lighting conditions, camera characteristics, and environmental factors, achieving high detection accuracy. The processing complexity is managed by implementing these corrections through efficient algorithms that run on mobile devices without requiring excessive computational resources.
Data Source
AI summary
An image analysis method and an apparatus thereof for assessment of PD (peritoneal dialysis) complications in peritoneal dialysis are provided. An analysis procedure is executed on an image under test of a dialysis bag, so as to obtain a color location in a color space corresponding to the image under test. A prompt signal is sent when the color locations obtained in a time period gradually become close to a disease warning range after executing the analysis procedure on a plurality of images under test.