Machine Learning Analysis of Label-Free Biological Samples
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Solution Overview
Problem
Current methods for analyzing biological samples using fluorescence imaging are time-consuming and may be harmful, as they require labeling with fluorescent dyes, which are not always feasible or desirable.
Innovation Solution
A system and method that uses a supervised machine learning approach to analyze biological samples by training a machine learning system with both label-free and fluorescent images, allowing for the generation of predicted cell characteristics from label-free images without the need for fluorescent imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fluorescent labeling is used to improve measurement precision of biological sample characteristics, then analysis accuracy is improved, but loss of time increases and harmful factors to the biological sample increase
Solution Approach 1:
The system performs preliminary action by training the machine learning model in advance using paired fluorescent and label-free images. This pre-training phase allows the model to learn the mapping between fluorescent characteristics and label-free appearances, enabling accurate analysis of label-free images without requiring real-time fluorescent labeling.
Solution Approach 2:
The invention creates a virtual copy of fluorescent imaging capability through machine learning. The trained model replicates the analytical power of fluorescent imaging by predicting fluorescent characteristics from label-free images, eliminating the need for physical fluorescent labels while maintaining measurement precision.
2Measurement precision
If fluorescent labeling is used to improve measurement precision, then analysis accuracy is improved, but object-affected harmful factors increase
Solution Approach 1:
The invention extracts and removes the harmful fluorescent labeling step from the analysis process. By using machine learning to predict fluorescent characteristics from label-free images, the system eliminates the need for fluorescent dyes that can be toxic to biological samples, while preserving the ability to analyze specific cellular characteristics.
Solution Approach 2:
The machine learning model serves as an intermediary that bridges the gap between label-free imaging and fluorescent analysis. It translates label-free image data into predictions of fluorescent characteristics, allowing the system to obtain accurate biological measurements without direct contact with harmful fluorescent labels.
3Measurement precision
If fluorescent imaging is used to obtain accurate cell characteristics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts and removes the fluorescent imaging subsystem from the imaging system. By using machine learning to predict fluorescent characteristics from label-free images, the invention eliminates the need for fluorescent light sources, filters, and detection systems, thereby reducing device complexity while maintaining measurement precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate analysis of biological samples using only label-free images, reducing the time and potential harm associated with fluorescent dye use, while maintaining the analytical advantages of fluorescence imaging.
Implementation Method 1
training a machine learning system using the first training image and the plurality of training cell characteristics to develop a trained machine learning system such that when the trained machine learning system is operated with the first training image as an input, the trained machine learning system generates a plurality of predicted cell characteristics
Data Source
AI summary
A system and method of analyzing a biological sample using an imaging system are disclosed. An image acquisition module instructs the imaging system to obtain a label free image of a training biological sample and in response receives a first training image. The image acquisition module also instructs the imaging system to cause the training biological sample to fluoresce and obtain an image of the training biological sample undergoing fluorescence, and in response receives a second training image. An analysis module analyzing the second training image to generate a plurality of training cell characteristics, wherein each of the plurality training cell characteristics is associated with one of a plurality of training cells that comprise the training biological sample. A training module trains a machine learning system using the first training image and the plurality of training cell characteristics to develop a trained machine learning system such that when the trained machine learning system is operated with the first training image as an input, the trained machine learning system generates a plurality of predicted cell characteristics that correspond to the plurality of training cell characteristics.