Flow Cytometry Cell Classification Using Machine Learning
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Solution Overview
Problem
The interpretation of clinical flow cytometry data is inherently difficult due to complex and heterogeneous immunophenotypes, and manual gating strategies are subjective, hindering advancements in clinical diagnostics.
Innovation Solution
A method involving machine learning models that classify cells based on flow cytometry data by receiving cell parameter information, inputting it into a machine learning model, and generating an indication of cell identity, including placement into cell classes and subclasses using algorithms like random forest and k-nearest neighbor models, with decision support systems for clinical decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual gating strategies are used for cell classification, then flexibility and adaptability are maintained, but subjectivity and interpretation difficulty increase
Solution Approach 1:
The patent replaces manual gating strategies (mechanical/human-operated system) with machine learning models (automated computational system). The machine learning models process flow cytometry data to automatically classify cells into populations, eliminating subjective human interpretation while maintaining adaptability through trained algorithms that can learn from diverse cell types and conditions.
2Measurement precision
If machine learning models are used for cell classification, then objectivity and accuracy are improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between raw flow cytometry data and final cell classification results. This intermediary consists of pre-trained machine learning models that have already learned complex patterns from training data, allowing them to process new data accurately without requiring the full complexity of the training process to be repeated. This intermediary layer simplifies the operational system while maintaining high accuracy.
3Ease of operation
If traditional flow cytometry analysis methods are used, then ease of operation is maintained, but diagnostic capability and insight quality are limited
Solution Approach 1:
The patent segments the cell classification process into distinct, automated stages handled by machine learning models. Each model specializes in specific aspects of cell identification and classification, breaking down the complex diagnostic task into manageable components. This segmentation enables the system to process and retain more diagnostic information from flow cytometry data while maintaining ease of operation through automated workflows that require minimal user intervention.
Data Source
AI summary
A method for classifying one or more cells comprises receiving data associated with the one or cells, the data including, for each respective cell, information associated with one or more measurable parameters of the respective cell; inputting at least a portion of the data into a machine learning model; and receiving, from the machine learning model, an indication of an identity of at least one of the one or more cells.