iPSC Colony Classification Using Brightfield Machine Learning
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Solution Overview
Problem
Existing methods for determining cell growth and health in cell cultures, such as those of induced pluripotent stem cells, are time-intensive, destructive, and lack standardization, leading to inefficiencies and resource waste.
Innovation Solution
Utilizing machine-learned models, particularly convolutional neural networks and random forest models, to analyze brightfield images of cell colonies for segmentation and classification, enabling early assessment of cell health and growth characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment methods are used to evaluate cell growth and health, then assessment accuracy can be maintained, but time consumption increases significantly and resources are wasted
Solution Approach 1:
The patent uses image copying and machine learning models to replicate and analyze cell colony characteristics without requiring manual intervention. The system captures images of cell colonies and uses trained models to automatically assess health status, growth rates, and other parameters, thereby maintaining assessment accuracy while dramatically reducing the time required compared to manual evaluation methods
Solution Approach 2:
The patent replaces manual mechanical assessment with automated digital imaging and machine learning analysis. Instead of visually inspecting cell colonies by hand, the system uses cameras to capture images and algorithms to process these images for automated characterization, eliminating the time-consuming manual process while maintaining or improving assessment precision
2Measurement precision
If destructive methods are used to determine cell growth status, then measurement accuracy improves, but cell viability and resource efficiency deteriorate
Solution Approach 1:
The patent creates digital copies of cell colonies through imaging rather than physically destroying them to assess growth status. The machine learning models analyze these digital images to determine cell health and growth characteristics, allowing repeated measurements without compromising cell viability or wasting biological materials
Solution Approach 2:
The patent introduces digital imaging and machine learning models as intermediary systems between the cell colonies and the assessment process. This intermediary layer allows non-destructive characterization by converting physical cell properties into digital data that can be analyzed without contacting or damaging the actual cells
3Reliability
If extended culturing periods are used to assess cell status, then measurement reliability improves, but productivity and resource efficiency worsen
Solution Approach 1:
The patent performs preliminary characterization of cell colonies at early timepoints using machine learning models, rather than waiting for extended culturing periods. The system can assess cell health, growth rates, and morphology relatively early in the culture process, allowing for timely decisions about which colonies to continue cultivating and which to discard, thereby improving productivity without sacrificing reliability
Solution Approach 2:
The patent implements continuous feedback loops where machine learning models continuously monitor and characterize cell colonies during the culture process. This ongoing assessment provides real-time information about cell health and growth status, enabling dynamic decisions to optimize resource allocation and improve overall productivity while maintaining reliable assessment through continuous monitoring rather than relying solely on extended timepoints
Data Source
AI summary
Systems and methods for using machine-learned models to characterize cell colonies are disclosed. In some embodiments, a machine-learned model includes a first model and, optionally, a second model. A first model may be a convolutional neural network for segmenting images. A second model may be a decision-tree-based and/or ensemble model, such as a random forest model, for example for grading cells or one or more cell cultures. Input for a second model may be based on output from a first model. Timepoint may also be used as an input to a first model and/or second model. Multi-frame images may be used as input to a machine-learned model. In some embodiments, each frame of a multi-frame image is input on a different input channel to a machine-learned model. Decisions about whether to continue culturing or not may be made based on characterization made using a machine-learned model.