Hematology Scatterplot Analysis for Automated State Detection
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Solution Overview
Problem
Traditional hematology analysis methods are time-consuming and require manual interpretation, limiting the efficiency and accuracy of blood parameter analysis for clinical decision-making.
Innovation Solution
A computer-implemented method using deep learning and machine learning models to analyze blood parameters from hematology analyzers, eliminating the need for manual processing and enabling automated state determination by interpreting scatterplots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual interpretation methods are used for hematology analysis, then diagnostic accuracy can be maintained through expert judgment, but analysis time and workload increase significantly
Solution Approach 1:
The patent replaces the mechanical manual interpretation system with an automated machine learning system. The deep learning model processes hematology parameters and scatterplot data to automatically determine clinical states, eliminating the need for manual expert analysis while maintaining or improving diagnostic accuracy through consistent algorithmic evaluation of all blood parameters.
Solution Approach 2:
The system enables self-service automation where the machine learning model independently analyzes hematology data without human intervention. The automated determination of clinical states from blood parameters allows the system to serve itself in the diagnostic process, reducing dependency on manual expert interpretation while accelerating analysis throughput.
2Reliability
If manual interpretation by skilled personnel is required, then diagnostic quality can be ensured, but productivity and throughput of the hematology laboratory decrease
Solution Approach 1:
The patent substitutes the manual expert interpretation mechanism with an automated machine learning system that processes hematology data. This replacement maintains diagnostic quality through validated algorithms while dramatically increasing laboratory throughput by eliminating the bottleneck of manual analysis and enabling parallel processing of multiple samples.
Solution Approach 2:
The system changes the operational parameters of the diagnostic process by transitioning from sequential manual review to automated parallel processing. The machine learning model evaluates multiple blood parameters simultaneously, changing the time and efficiency characteristics of the diagnostic workflow while maintaining reliability through consistent application of diagnostic criteria.
3Device complexity
If only a few derived statistics are used for clinical decisions, then analysis is simpler, but the enormous potential of full blood cell measurement information remains untapped
Solution Approach 1:
The patent applies multi-functionality by using a single machine learning model to process and integrate all types of hematology measurement data including blood cell counts, scatterplot patterns, and derived parameters. This universal approach extracts clinical information from the complete dataset rather than relying on selected statistics, maximizing information utilization while managing complexity through integrated analysis.
Solution Approach 2:
The system creates a composite analytical approach by combining multiple data sources (blood parameters, scatterplot data, cell morphology) into a unified machine learning model. This composite method integrates diverse measurement types to extract comprehensive clinical information, utilizing the full potential of hematology measurements rather than relying on individual parameters in isolation.
4Productivity
If automated analysis methods are implemented, then productivity and speed increase, but the complexity of the analysis system and model interpretation increases
Solution Approach 1:
The patent replaces manual analysis with automated machine learning systems that process hematology data at high speed. While the underlying model complexity increases, the automation eliminates manual bottlenecks and provides consistent, rapid analysis across all samples, with the complexity managed through established machine learning frameworks and validation protocols.
Data Source
AI summary
A computer-implemented method for determining states in vivo and in vitro by analyzing blood parameters, including obtaining blood parameters of a blood sample by a hematology analyzer, the blood parameters including quantitative and qualitative measurement variables, and the measurement variables include properties of individual cells, and the individual cells comprise blood cells. The computer-implemented method further includes creating a scatterplot having at least two axes, and each axis of the scatterplot comprises a different measurement variable; and determining an in-vivo and/or in-vitro and/or post-mortem state by a deep learning model and/or a machine learning model. The input variable for the deep learning model includes a scatterplot, and the input variable for the machine learning model includes 1D vector. The 1D vector is created by vectorizing the scatterplot; and automatically generating a report including the result regarding the determination of the state.