Specimen Classification Networks for Hemolysis, Icterus, and Lipemia
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automated diagnostic systems face challenges in accurately and efficiently determining the presence and degree of hemolysis, icterus, and lipemia in serum or plasma portions of blood specimens, which can lead to erroneous test results due to subjective manual inspection and complex, computationally burdensome image processing methods.
Innovation Solution
A method and apparatus using a segmentation convolutional neural network (SCNN) with a deep semantic segmentation network (DSSN) to capture multi-spectral, multi-exposure images from multiple viewpoints, followed by verification networks to predict and verify the classification of hemolysis, icterus, and lipemia, enabling robust characterization of specimen integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual visual inspection is used to evaluate specimen integrity, then the process is simple and requires minimal equipment, but it is subjective, labor intensive, and prone to human error
Solution Approach 1:
The patent replaces manual visual inspection with an automated image capture device and machine learning classification system. The image capture device objectively captures specimen images, and the machine learning model automatically classifies specimen integrity (normal, hemolysis, icterus, lipemia), eliminating human subjectivity and error while maintaining operational simplicity through automation.
2Reliability
If automated machine-vision inspection is used to evaluate specimen integrity, then objectivity and automation are improved, but the system becomes more complex and computationally burdensome
Solution Approach 1:
The patent segments the inspection process into distinct components: image capture, image processing, machine learning classification, and verification. This segmentation allows each component to be optimized independently and reduces overall system complexity by dividing the automated inspection into manageable functional blocks.
Solution Approach 2:
The patent uses machine learning models that process image parameters (color, texture, intensity) to classify specimen integrity. By transforming visual characteristics into quantifiable parameters and using trained neural networks to interpret them, the system achieves reliable automated inspection without requiring overly complex hardware or computational infrastructure.
3Device complexity
If manual visual inspection is used, then equipment requirements are minimal, but time consumption and labor requirements increase
Solution Approach 1:
The patent replaces manual visual inspection with automated image capture and machine learning classification systems. This substitution dramatically increases inspection speed and productivity while reducing labor requirements, as the automated system can rapidly process multiple specimens without human intervention.
4Measurement precision
If automated image processing is used to characterize specimen integrity, then inspection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by capturing images at standardized viewpoints and pre-processing them through automated image enhancement and normalization techniques. This preparation before classification allows the machine learning model to quickly and accurately characterize specimen integrity, reducing overall processing time while maintaining high accuracy.
Data Source
Figure 1A~1B
Figure 1C~2
Figure 3A
AI summary
A method of characterizing a specimen as containing hemolysis, icterus, or lipemia is provided. The method includes capturing one or more images of the specimen, wherein the one or more images include a serum or plasma portion of the specimen. Pixel data is generated by capturing the image. The pixel data of the one or more images of the specimen is processed using a first network executing on a computer to predict a classification of the serum or plasma portion, wherein the classification comprises hemolysis, icterus, and lipemia. The predicted classification is verified using one or more verification networks. Quality check modules and specimen testing apparatus adapted to carry out the method are described, as are other aspects.