AI Cell Selection Using Homogeneity Classification and Visualization
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Solution Overview
Problem
Existing stem cell therapeutics face challenges in efficacy control and evaluation due to variations in proliferation and differentiation based on cell sources, requiring a cell selection technology using artificial intelligence to ensure functional stem cells are selected and differentiated effectively.
Innovation Solution
A cell selection device and method utilizing pre-processing, model generation, cell classification, and visualization units to classify cells based on homogeneity through artificial intelligence, employing pre-trained learning models and normalization techniques to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cell analysis methods are used, then detailed individual cell analysis can be performed, but it consumes a lot of time and money
Solution Approach 1:
The patent replaces traditional manual or mechanical cell analysis methods with an artificial intelligence-based image analysis system. The AI model automatically processes cell images to determine homogeneity and classify cells, substituting the mechanical/manual analysis process with an intelligent automated system that achieves both high precision and efficiency
Solution Approach 2:
The patent uses image copying and processing techniques where cell images are captured, processed through preprocessing steps (normalization, augmentation), and analyzed by the AI model. Multiple copies and transformations of cell images are created to enhance training data without requiring additional physical samples or time-consuming experiments
2Measurement precision
If traditional cell analysis methods are used, then individual cell details can be examined, but the cost is very high
Solution Approach 1:
The patent replaces expensive traditional cell analysis methods with an AI-based image analysis system. By using automated image processing and machine learning algorithms, the system eliminates the need for costly manual analysis, specialized equipment, and extensive laboratory resources while maintaining high measurement precision
Solution Approach 2:
The patent employs cost-effective digital image processing techniques instead of expensive physical analysis methods. The AI model processes digital cell images through various preprocessing operations (normalization, augmentation, transformation) that are computationally inexpensive compared to traditional laboratory analysis costs
3Adaptability or versatility
If stem cells from different sources are used, then more cell therapeutic options are available, but efficacy control becomes difficult
Solution Approach 1:
The patent changes the parameter of cell evaluation from subjective or manual assessment to objective AI-based homogeneity measurement. By using consistent image processing parameters and AI evaluation criteria across different cell sources, the system maintains reliable efficacy control while accommodating diverse cell sources including induced pluripotent stem cells, embryonic stem cells, and mesenchymal stem cells
Solution Approach 2:
The patent implements a feedback mechanism where the AI model analyzes cell images, determines homogeneity levels, and provides classification results that feed back into the cell selection process. This closed-loop system ensures consistent quality control across different cell sources by automatically identifying and selecting cells that meet predefined homogeneity criteria
Data Source
AI summary
The present disclosure relates to a cell selection apparatus and method and, in particular, may provide an apparatus that classifies cells by using artificial intelligence, on the basis of the homogeneity of cells from an image pre-processed from image data obtained by capturing images of cells, and marks parts that have an important influence on the classification results, thereby increasing the accuracy of cell selection. In addition, a reliable selection basis can be provided by visualizing the basis for cell selection.