Flow Cytometry Classifier Training Without Real-Time Delay
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional manual gating in flow cytometry lacks reproducibility due to human variability, while machine learning-based methods introduce training delays that hinder real-time application in flow cytometry analysis.
Innovation Solution
Implement asynchronous training for machine learning classifiers in flow cytometry, allowing training to proceed in the background while enabling immediate use of classification results, with real-time feedback on classifier performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning-based classification is implemented in flow cytometry, then classification accuracy and reproducibility are improved, but training time delays real-time application
Solution Approach 1:
The system performs preliminary training actions by pre-training classification models on historical flow cytometry data before actual analysis is needed. This allows the model to be ready for immediate deployment, eliminating training delays during critical real-time applications while maintaining high classification accuracy and reproducibility.
Solution Approach 2:
The system implements continuous training where the classification model is trained incrementally on new data while simultaneously performing classification tasks. This continuous learning approach ensures the model remains accurate and reproducible without requiring complete training interruptions, thus maintaining real-time application capability while improving classification reliability over time.
2Productivity
If manual gating is used for cell classification, then immediate classification results are obtained, but human variability reduces reproducibility
Solution Approach 1:
The system implements self-service by enabling the classification model to automatically adjust and refine its own parameters through continuous learning from new data without requiring manual intervention. This automated self-optimization maintains high classification speed while improving reproducibility by eliminating human variability in gate drawing and parameter selection.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results and performance metrics are continuously monitored and fed back into the training process. This feedback loop allows the model to learn from its own performance, automatically adjusting to improve reproducibility while maintaining the rapid classification speed characteristic of automated systems.
Data Source
AI summary
Systems, devices, and method for asynchronous training for classification of cellular subsets for flow cytometry. A training file is identified for training a classifier. A gate associated with the training file is received for training the classifier and a model for training the classifier is received. The classifier training begins and, concurrent with training of the classifier: flow cytometry files are classified and an accuracy of the classifier is monitored. A determination is made whether the gate should be adjusted based on the accuracy and a determination is made whether training of the classifier is complete.