Deep Learning Foci Counting for Flavivirus Infectivity Testing
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Solution Overview
Problem
Conventional manual counting of virus foci in immunofocus assays for vaccine quality control is time-consuming, error-prone, and susceptible to interpersonal variations, making it challenging to ensure data integrity and accuracy.
Innovation Solution
A computer-implemented method using trained deep learning algorithms to analyze image data of immunofocus assays for accurately and efficiently determining the number of foci, reducing errors and interpersonal variation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual counting of virus foci is used, then simplicity of operation is maintained, but productivity is reduced and measurement precision deteriorates
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated image processing system using deep learning algorithms. The system captures images of virus foci in immunofocus assays and uses computer vision to automatically count and analyze them, eliminating the need for manual observation and counting by researchers.
Solution Approach 2:
The patent creates a digital copy (image) of the virus foci and processes this copy through algorithmic analysis rather than directly manipulating or counting the physical foci. This allows multiple analyses of the same sample without additional manual effort and enables precise digital measurement and recording of results.
2Reliability
If manual counting is performed, then equipment requirements are minimized, but reliability deteriorates due to interpersonal variations
Solution Approach 1:
The patent replaces human visual inspection and manual counting with automated image processing algorithms. This substitution eliminates interpersonal variations in counting methods, subjective interpretation differences, and human error, providing consistent and reproducible results across different laboratories and researchers.
Solution Approach 2:
The system provides automated feedback through standardized image processing and analysis, generating objective numerical results that can be verified and reproduced. The deep learning algorithms learn from training data and provide consistent classification and counting feedback, reducing variability compared to manual methods.
3Measurement precision
If automated image processing is implemented, then productivity is improved and reliability is enhanced, but device complexity increases
Solution Approach 1:
The patent employs advanced image processing techniques including deep learning algorithms to automatically analyze and count virus foci. The system captures images, processes them through multiple computational steps including segmentation, feature extraction, and classification, and generates precise quantitative results that exceed the accuracy of manual counting methods.
Data Source
AI summary
A computer-implemented method of determining infectivity of a flavivirus-containing sample is described. The method includes receiving (S1), with a computing device (10), image data indicative of an image of at least a part of a container (50, 50a, 50b) comprising a composition containing host cells with one or more foci (51) generated by infecting the host cells with the flavivirus over an incubation period and optionally subsequent staining of the incubated host cells. The method further includes determining (S2) a number of foci (51) in the at least part of the container (50, 50a, 50b) based on processing the received image data with at least one trained deep learning algorithm of the computing device (10), wherein the number of foci (51) is indicative of the infectivity of the flavivirus in the sample.