Image-Based Virus Titer Prediction for Rapid Plaque Assays
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Solution Overview
Problem
Current virus plaque assays take several days to complete, and there is a need for rapid methods to concurrently determine both total and infectious virus particle counts, which is crucial for applications like vaccine development and gene therapy safety.
Innovation Solution
A machine learning model is trained using image-based methods to predict virus titer in cell cultures within hours by analyzing time sequences of images, allowing for early prediction of virus titer through an integrated imaging system and analytical instrument.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard plaque assay is used to measure virus titer, then measurement precision is improved, but loss of time increases significantly
Solution Approach 1:
The patent applies preliminary action by performing early imaging of cell cultures at multiple time points before the standard endpoint (2-14 days). Machine learning models are trained on these early images to predict final virus titers, allowing the assay to be effectively completed in hours rather than days while maintaining measurement precision through the predictive modeling approach.
2Loss of time
If early prediction of virus titer is implemented, then loss of time is reduced, but measurement precision may deteriorate
Solution Approach 1:
The patent implements feedback by training machine learning models using ground truth virus titer data obtained from standard endpoint assays. The models learn from the relationship between early imaging data and final outcomes, continuously improving prediction accuracy. This feedback loop enables early time-point predictions to achieve measurement precision comparable to endpoint assays while reducing assay duration.
Solution Approach 2:
The patent applies partial action by using machine learning models to predict virus titers at partial time points (6-8 hours) rather than waiting for complete plaque formation. The models are trained on datasets that include multiple time points, allowing them to accurately predict final titers from intermediate states, thus achieving sufficient measurement precision without completing the full traditional assay duration.
3Productivity
If machine learning model is trained on multiple time points, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent replaces the mechanical/manual process of endpoint assessment with machine learning-based image analysis. Automated machine learning models process images and predict virus titers, eliminating manual plaque counting and enabling high-throughput analysis of multiple samples and time points. This substitution of automated computational methods for manual procedures significantly improves productivity while the complexity is managed through software-based solutions.
Data Source
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AI summary
A method is described for training a machine learning model to predict virus titer from an image or a sequence of images of a cell culture containing a virus population. The trained machine learning model allows a prediction of virus titer to be made much earlier than in the standard virus plaque assay, for example in 6 or 8 hours after initial inoculation of the cell culture with the virus sample. The method includes the steps of: (1) obtaining a training set in the form of a plurality of sets of images of virus-treated cell cultures from a plurality of experiments at one or more time points from a start time to to a final time tfinal, (2) for each experiment, recording at least one numeric virus titer readout of the virus-treated cell culture at the final time tfinal, (3) processing all the images in the training set to acquire a numeric representation of each image, and (4) training one or more machine learning models to make a prediction of a final virus titer on the training set numeric representations.