Bright-Field Cell Seeding Control for CD34 Quantity Estimation
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Solution Overview
Problem
Existing methods for producing pluripotent stem cells from CD34 positive cells are costly and lack a simple, accurate way to estimate and adjust the number of cells to be seeded, leading to inefficiencies in the production process.
Innovation Solution
An information processing device and cell processing apparatus that utilize a machine learning model to analyze bright field images for estimating the quantity of CD34 positive cells and calculate the appropriate seeding quantity, incorporating performance indexes to ensure accurate and efficient production of pluripotent stem cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to produce pluripotent stem cells from CD34 positive cells, then the production process can be completed, but it is costly and lacks accuracy in estimating cell quantities
Solution Approach 1:
The patent replaces conventional mechanical counting methods with image processing and machine learning algorithms. The system captures images of cells and uses automated algorithms to estimate CD34 positive cell quantities, eliminating the need for manual counting and complex laboratory equipment while improving measurement precision.
Solution Approach 2:
The patent creates a digital copy of the cell sample through imaging, then analyzes this copy using machine learning models to estimate cell quantities. This allows accurate measurement without physically manipulating or consuming the actual cell sample, reducing complexity and cost.
2Productivity
If manual cell counting methods are used, then equipment costs are reduced, but accuracy and efficiency of cell quantity estimation deteriorates
Solution Approach 1:
The patent replaces manual cell counting with automated image processing systems that use machine learning algorithms. This substitution dramatically improves productivity by processing images rapidly without human intervention while simultaneously enhancing measurement precision through sophisticated pattern recognition capabilities.
Solution Approach 2:
The system performs self-analysis by automatically capturing cell images, processing them through machine learning models, and generating cell quantity estimates without requiring external manual intervention. This self-service capability boosts productivity while maintaining high accuracy.
3Loss of substance
If inaccurate cell quantity estimation is used, then the production process is simpler, but cell loss and operational costs increase
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model continuously refines its cell quantity estimates based on image analysis. This feedback loop ensures high measurement precision in seeding quantity determination, allowing accurate control of cell numbers to minimize cell loss while optimizing production outcomes.
Solution Approach 2:
The system performs preliminary cell quantity estimation before the seeding process using image analysis and machine learning. This advance knowledge allows precise determination of the required seeding quantity, preventing both cell shortage and excess cell loss during the production process.
Data Source
AI summary
According to one embodiment, an information processing device includes an image acquisition unit, an estimation unit, and a calculation unit. The image acquisition unit acquires a bright field image that captures a cell group including cells of a plurality of types. Based on the bright field image, the estimation unit estimates a specific cell quantity regarding a quantity of cells of a specific type among the plurality of types. Based on the specific cell quantity, the calculation unit calculates a seeding quantity of cells or suspension including the cell of the specific type to be used in the following process.