AI Virus Strain Analysis via CNN Image Correlation
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Solution Overview
Problem
Current methods for analyzing virus strains, such as sequencing and tissue culture assays, are labor-intensive and do not efficiently identify correlations between different strains, which are crucial for predicting and managing pandemics.
Innovation Solution
The use of image-based artificial intelligence systems, specifically convolutional neural networks (CNNs), to analyze images from assay wells and identify correlations between different virus strains by recognizing patterns associated with each strain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sequencing and tissue culture assays are used to analyze virus strains, then comprehensive viral characteristics can be obtained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical processes (sequencing operations, tissue culture handling, microscopic examination) with an automated digital imaging system using convolutional neural networks. The CNN-based system automatically captures, processes, and analyzes images of viral cytopathic effects, eliminating the need for labor-intensive manual sequencing and tissue culture assays while maintaining comprehensive viral characterization capabilities
Solution Approach 2:
The system enables self-service analysis through automated image capture and processing. The convolutional neural network autonomously analyzes images of viral infections in cell cultures, automatically identifying and characterizing viral strains without requiring continuous human intervention for each analysis step, thereby significantly reducing both labor intensity and analysis time
2Loss of information
If manual analysis methods are used to identify correlations between virus strains, then detailed strain comparison is possible, but the ability to efficiently identify correlations across multiple strains is limited
Solution Approach 1:
The patent replaces manual correlation analysis with an automated computational system. The convolutional neural network processes images from multiple viral strain experiments simultaneously, automatically extracting features and identifying correlations between strains based on their cytopathic effect patterns. This digital system maintains comprehensive strain comparison capabilities while enabling high-throughput analysis of numerous strains in parallel
Solution Approach 2:
The CNN-based system serves multiple functions: it captures images, identifies individual viral strains, compares strain characteristics, and identifies correlations across different strains. This multi-functional approach allows the system to handle comprehensive strain comparison and correlation identification in a unified automated platform, significantly increasing productivity compared to separate manual analysis processes
Data Source
AI summary
Disclosed systems and methods include generating augmented images based on an image, processing, using two or more neural networks, each of the augmented images and the image, determining, for each of the two or more neural networks, a fitness metric, and determining a performance of each of the two or more neural networks based on the determined fitness metric. Disclosed systems and methods also include selecting one or more neural networks, processing, using the selected one or more neural networks, a plurality of images, generating, with the selected one or more neural networks, an association of each image of the plurality of images with one of a plurality of clusters, generating a correlation coefficient, and determining a degree of correlation between a first variant and a second variant of the plurality of variants.