Impedance-Based Cell Typing with Neural Network Imaging
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Solution Overview
Problem
Traditional methods for cell typing, particularly for cancer cells, often require bulky and complex microscopic equipment, making automatic determination of cell types inefficient and impractical.
Innovation Solution
A method using impedance sensing and neural networks to generate virtual images of cells based on impedance measurements, allowing for the automatic classification of single cells and cell populations, utilizing microfluidic devices and portable electronics for impedance measurement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional microscopic equipment is used for cell typing, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces traditional optical mechanical microscopy systems with an electrical impedance-based measurement system. The impedance cytometer uses electrical fields to interact with cells, measuring impedance changes as cells pass through a sensing region between electrodes, thereby substituting complex optical mechanics with simpler electrical measurement principles while maintaining cell identification capability
Solution Approach 2:
The patent creates virtual images of cells through computational processing of impedance data. A neural network trained on impedance data generates synthetic microscopic images that replicate the visual appearance of actual cell images, allowing traditional image-based analysis methods to be applied to impedance-derived data without requiring physical microscopes
2Measurement precision
If traditional microscopic equipment is used for cell typing, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements automated cell typing through a self-service system where the impedance cytometer continuously measures impedance signals and a neural network automatically classifies cell types without human intervention. The system processes cells in real-time as they flow through the device, providing automatic determination of cell types including cancer cell identification, eliminating the need for manual microscopic examination
Solution Approach 2:
The patent replaces manual optical microscopy operations with automated electrical impedance measurement and computational classification. The system uses electrical fields to probe cells and neural networks to interpret signals, substituting the manual skill-based operation of traditional microscopes with automated electronic systems that improve ease of operation while maintaining precision
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 accurate and efficient cell typing without the need for expensive and cumbersome microscopes, providing high similarity in virtual imagery and enabling disease diagnosis and treatment tracking with portable, disposable microfluidic devices.
Implementation Method 1
measuring single cell impedance observation data that indicates a time series of impedance measurements during an observation time in which a single biological cell traverses a first gap between a first pair of electrodes
Data Source
AI summary
Techniques for automatically measuring cell type based on electrical impedance includes single cell type or population cell types. Imaging a single cell includes measuring impedance time series while a single cell traverses a gap between a pair of electrodes in a microfluidic channel. A virtual image of the single cell is generated using a trained neural network and the measured impedance time series. Each training instance includes impedance time series of a training instance cell and a microscopic image of the cell. Automatically determining cell type of a population includes measuring population impedance time series while multiple cells of a sample traverse the gap. A measured probability density function (PDF) of amplitudes of isolated extrema in the population is generated. A first cell type in the sample is determined automatically based on the measured PDF and a database storing a PDF for each cell type of multiple cell types.