Automated Hematological Abnormality Detection via High-Dimensional Vector Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current multiparameter flow cytometry (MFC) methods for detecting minimal residual disease (MRD) in hematological malignancies, such as acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), face challenges including lack of inter-lab standardization, manual gating processes, and high dependence on skilled personnel, leading to inefficiencies and variability in diagnosis.
Innovation Solution
An automated system utilizing a hematological abnormality classifier trained with support vector machines (SVM) on high-dimensional flow cytometry data, converting flow cytometry data matrices into high-dimensional vectors, and concatenating them to classify samples as normal or abnormal, providing an objective and rapid diagnostic tool for MRD detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual gating processes are used for flow cytometry data analysis, then diagnostic accuracy can be maintained through expert interpretation, but productivity is reduced and inter-lab standardization is compromised
Solution Approach 1:
The system enables automated self-service analysis where the flow cytometry data is automatically processed through machine learning classifiers without requiring manual gating by skilled personnel. The automated pipeline transforms raw flow cytometry data into diagnostic classifications independently, eliminating the bottleneck of manual interpretation while maintaining consistent diagnostic accuracy across different laboratories.
2Measurement precision
If manual gating processes are used for flow cytometry data analysis, then diagnostic accuracy can be maintained through expert interpretation, but dependence on skilled personnel increases
Solution Approach 1:
The patent replaces the mechanical/manual gating process performed by skilled personnel with an automated computational system. Machine learning classifiers and automated algorithms substitute for human expert interpretation, transforming the manual operation into an automated digital process that does not require specialized manual gating skills while maintaining diagnostic accuracy.
3Productivity
If automated classification systems are implemented, then productivity and standardization are improved, but device complexity increases
Solution Approach 1:
The automated system is segmented into distinct modular components: data preprocessing module, feature extraction module, machine learning classification module, and result interpretation module. This segmentation allows each component to be independently optimized and maintained, reducing the operational complexity despite the overall system's advanced capabilities. The modular architecture enables incremental implementation and simplifies troubleshooting.
4Measurement precision
If high-dimensional vector transformation is applied to flow cytometry data, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The system transforms flow cytometry data from traditional low-dimensional representations into high-dimensional vector spaces, changing the parameter representation to enable more precise classification. This parameter transformation allows the machine learning algorithms to capture subtle patterns in the data that would be invisible in conventional analyses, improving diagnostic accuracy while the computational complexity is managed through optimized algorithms and hardware acceleration.
Data Source
AI summary
This application relates generally to automated systems and associated methods for identifying hematological abnormalities. An automated system can include at least one processor that, in operation, is configured to: receive, from a flow cytometer, a flow cytometry data matrix characterizing a tube that is associated with a sample; convert the flow cytometry data matrix into a high dimensional vector; produce a single sample high dimensional vector including a concatenation of multiple high dimensional vectors associated with the sample, wherein the multiple high dimensional vectors comprise the tube high dimensional vector; assemble a training data set including multiple sample high dimensional vectors; receive, from a datastore, outcome information including respective labels associated with each of the multiple sample high dimensional vectors; and train a classifier based on the training data set and the outcome information.