Automated Cell Evaluation via Imaging Flow Cytometry and Reference Database
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Solution Overview
Problem
Current cell evaluation methods face challenges in accurately and efficiently classifying and evaluating individual cells due to reliance on subjective human evaluation in supervised learning and high costs and low throughput in biochemical methods, with unsupervised learning lacking explainability and biological relevance.
Innovation Solution
A system and method that physically measures cells using imaging flow cytometry, referencing stored relevance levels between measurement information and biological data to automatically evaluate and classify cells, utilizing a database to associate physical measurement data with biological information for accurate and high-speed evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning is used for cell classification, then classification can be performed with human expertise, but the evaluation is limited to human determination range and contains subjective bias
Solution Approach 1:
The patent creates a database that copies and stores reference measurement information along with biological measurement information. Instead of relying on human experts to classify cells, the system copies reference data and uses automated algorithms to compare new cell measurements against this database, achieving both high accuracy through reference comparison and full automation through computational processing
Solution Approach 2:
The patent replaces the mechanical/human-based classification system with an automated computational system. Human expertise is encoded into the database structure and reference measurement information, while the actual classification process is performed by computers using algorithms that automatically compare new measurements against the stored references, eliminating human subjectivity and bias
2Extent of automation
If unsupervised machine learning is used for cell classification, then automation is achieved without human intervention, but the determination criteria are not explainable and biological meaningfulness cannot be verified
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously compares automated classification results against the stored reference measurement information and biological measurement information. This feedback loop allows the system to verify that automated classifications are biologically meaningful by checking consistency with known biological data, and can adjust or refine classifications accordingly
Solution Approach 2:
The patent introduces reference measurement information as an intermediary between the automated classification process and biological validation. This intermediary layer consists of pre-stored reference data that bridges the gap between raw measurement data and biological interpretation, allowing automated algorithms to produce results that can be verified against biologically relevant references
3Measurement precision
If biochemical measurement methods are used for cell analysis, then accurate classification can be achieved, but the cost is high and throughput is low
Solution Approach 1:
The patent copies and stores reference measurement information in a database, enabling rapid automated comparison of new cell measurements against the reference data. This copying approach allows for high-throughput analysis by eliminating the need for expensive biochemical measurements for each individual cell, while maintaining accuracy through reference-based comparison
Solution Approach 2:
The patent replaces expensive biochemical measurement systems with an automated optical measurement and computational analysis system. By using imaging flow cytometry to capture cell morphology and fluorescence data, then processing this data through automated algorithms that reference stored measurements, the system achieves both high throughput and accurate classification without the high costs and low throughput of biochemical methods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables high-speed, accurate, and automated cell classification and evaluation without human intervention, leveraging physical measurement data to determine cell type and characteristics based on biologically relevant information, effectively utilizing large volumes of imaging flow cytometry data.
Implementation Method 1
irradiating microparticles such as cells as the observation targets with an excitation light to obtain a total amount of a fluorescence intensity and/or scattered light emitted from the individual cells
Implementation Method 2
obtain a total amount of a fluorescence intensity and/or scattered light emitted from the individual cells
Data Source
AI summary
A cell evaluation system includes physical measurement unit, a database, and evaluation unit. The evaluation unit refers to a relevance stored in the database, searches reference measurement information based on measurement information of a cell newly measured via the physical measurement unit, and evaluates the cell with biological measurement information associated with the searched reference measurement information.