Hematology Shape Parameter for Flow Cytometry Noise
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Solution Overview
Problem
Flow cytometry data interpretation is limited by noise, which can skew standard deviation calculations and fail to capture multivariate population characteristics, leading to inaccurate diagnosis.
Innovation Solution
A method is introduced to calculate an improved shape parameter by generating a histogram from flow cytometry data, smoothing it to reduce noise, and computing shape characteristics along slicing lines at various angles, allowing for more accurate comparison of data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If standard deviation is used to characterize blood cell population shape, then the calculation is simple and provides a scalar value, but it is significantly impacted by noise and cannot capture multivariate population characteristics
Solution Approach 1:
The patent transitions from using a single scalar standard deviation value to employing multiple shape parameters that capture different aspects of the population distribution (mean, standard deviation, skewness, kurtosis, and histogram-based parameters). This dimensional expansion allows the system to characterize multivariate population characteristics while maintaining computational feasibility through systematic calculation methods.
Solution Approach 2:
The patent segments the population characterization into multiple independent shape parameters rather than relying on a single aggregate metric. By dividing the description into distinct parameters (central tendency, dispersion, symmetry, tail behavior, and histogram-derived features), the system can identify which specific parameters are affected by noise and make targeted adjustments or weightings in the abnormality detection algorithm.
2Ease of operation
If standard deviation is used to detect abnormalities, then the method is straightforward to implement, but it fails to distinguish between populations with identical standard deviations but different histogram shapes
Solution Approach 1:
The patent adds multiple dimensions to the population characterization by introducing skewness, kurtosis, and histogram-based parameters alongside the standard deviation. These additional parameters preserve information about the histogram shape that would otherwise be lost, enabling the system to distinguish between populations that have identical standard deviations but different distribution shapes.
Solution Approach 2:
The patent changes the parameter set used for abnormality detection from a single standard deviation metric to a comprehensive set including mean, standard deviation, skewness, kurtosis, and histogram-derived parameters. This parameter expansion allows the system to detect abnormalities based on changes in distribution shape while maintaining implementation feasibility through automated calculation of all parameters.
3Productivity
If noise is present in flow cytometry data, then data collection is straightforward, but the noise skews standard deviation calculations and deteriorates diagnostic usefulness
Solution Approach 1:
The patent applies preliminary smoothing operations to the histogram data before calculating shape parameters. By preprocessing the data to reduce noise effects prior to parameter extraction, the system preserves diagnostic reliability while maintaining efficient data collection. The smoothing step is performed automatically as part of the parameter calculation pipeline.
Solution Approach 2:
The patent uses multiple shape parameters that provide feedback about different aspects of the population distribution. By calculating and analyzing multiple parameters simultaneously, the system can identify when noise is affecting specific parameters and adjust the interpretation accordingly, improving overall diagnostic reliability through cross-validation of multiple metrics.
Data Source
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AI summary
Systems, methods, and computer program products are provided for describing characteristics of a data sample. This description is used to represent the shape of a histogram of the data sample.