Automated Dot Plot Analysis for Blood Condition Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current hematology analyzers require human intervention for analyzing blood samples to identify conditions like left shift or small-pathologic red blood cells, which can be time-consuming and prone to errors, especially in identifying subtle changes indicative of inflammation or pathologic processes.
Innovation Solution
A system and method that processes and analyzes two-dimensional dot plots without human intervention, using a processor to determine spatial distributions of white blood cells and red blood cells, identifying left shift or small-pathologic red blood cells by analyzing geometric shapes, centroids, standard deviations, and spatial density bands, and providing indications based on configurable thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human intervention is used to analyze blood samples and identify conditions like left shift or small-pathologic red blood cells, then measurement precision can be maintained through expert judgment, but productivity decreases due to time-consuming manual analysis
Solution Approach 1:
The system performs self-service by automatically analyzing dot plots and identifying blood conditions without requiring human intervention. The processor executes algorithms that autonomously detect left shift and small-pathologic red blood cells, eliminating the need for manual review while maintaining consistent diagnostic accuracy.
Solution Approach 2:
The patent replaces the mechanical system of manual visual analysis with an automated computational system. The processor substitutes human experts by executing algorithms that analyze dot plot data, compare spatial distributions against reference ranges, and generate diagnostic indications, thereby increasing productivity while preserving measurement precision through consistent algorithmic application.
2Measurement precision
If human experts manually analyze dot plots to identify subtle changes indicative of inflammation, then measurement precision is improved through expert pattern recognition, but loss of time increases due to manual review requirements
Solution Approach 1:
The system autonomously performs the analysis task that previously required human experts. The processor automatically detects subtle changes in dot plot patterns indicative of inflammation by comparing spatial distributions against established reference ranges, eliminating time loss while maintaining detection accuracy through programmed pattern recognition.
Solution Approach 2:
The system performs preliminary analysis of dot plots automatically before any potential human review. By pre-processing the data and generating diagnostic indications through automated comparison with reference ranges, the system eliminates the need for time-consuming manual review while preserving the ability to detect subtle pathological changes.
3Productivity
If automated analysis without human intervention is implemented, then productivity increases through faster processing, but measurement precision may decrease due to lack of expert judgment
Solution Approach 1:
The patent replaces human expert judgment with a computational system that uses programmed algorithms for pattern recognition. The processor analyzes dot plot spatial distributions and compares them against reference ranges established from expert data, achieving both high productivity through automated processing and maintained precision through algorithmic consistency and objective comparison criteria.
Solution Approach 2:
The system incorporates feedback mechanisms by comparing automated analysis results against reference ranges derived from expert judgment. This feedback loop ensures that automated measurements remain aligned with expert standards, maintaining measurement precision while achieving the productivity benefits of automation through rapid iterative processing.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Automates the analysis of blood samples, reducing human error and increasing efficiency in identifying conditions such as left shift and small-pathologic red blood cells, enabling timely and accurate detection of inflammation and pathologic processes.
Implementation Method 1
Light from the light source is absorbed and scattered by the components in a manner that is dictated by associated stains in the solution and/or the size and morphology of the components
Implementation Method 2
Light from the light source is absorbed and scattered by the components
Data Source
AI summary
Disclosed are approaches for analyzing a two-dimensional (2D) dot plot, without human intervention, to identify conditions in a hematology sample. The analyses operate to provide indications of left shift and/or small-pathologic red blood cells based on spatial distribution of one or more groups of dots in the 2D dot plot. Spatial distribution of a group of white blood cell dots is analyzed to provide an indication of left shift. Spatial distribution of a group of red blood cell dots is analyzed to provide an indication of presence of small-pathologic red blood cells.