ArticleAnalytical and bioanalytical chemistry2020
A neural network approach for real-time particle/cell characterization in microfluidic impedance cytometry.
Article in Analytical and bioanalytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Event-Preserving Neural Network Denoising for Impedance Flow Cytometry.Nanomaterials (Basel, Switzerland) · 2026Article
- A Derivative-Based Framework for Real-Time Signal Processing and Event Detection in Impedance Flow Cytometry.Sensors (Basel, Switzerland) · 2025Article
- Enhancing cell characterization with microfluidics and AI: a comprehensive review of mechanical, electrical, and hybrid techniques.Biotechnology reports (Amsterdam, Netherlands) · 2025Review
- On-Chip Integration of Impedance Cytometry for Inline Optimization of Dielectrophoretic Separations on Multiple Cellular Biophysical Metrics.ACS sensors · 2025Article
- Neural Network-Enabled Multiparametric Impedance Signal Templating for High throughput Single-Cell Deformability Cytometry Under Viscoelastic Extensional Flows.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Microfluidic-Based Electrical Operation and Measurement Methods in Single-Cell Analysis.Sensors (Basel, Switzerland) · 2024Review
- Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry.Biomicrofluidics · 2024Article
- Machine learning implementation strategy in imaging and impedance flow cytometry.Biomicrofluidics · 2023Review
- Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm.Micromachines · 2023Article
- Rapid Assessment of Susceptibility of Bacteria and Erythrocytes to Antimicrobial Peptides by Single-Cell Impedance Cytometry.ACS sensors · 2023Article
- Supervised learning on impedance cytometry data for label-free biophysical distinction of pancreatic cancer cells versus their associated fibroblasts under gemcitabine treatment.Biosensors & bioelectronics · 2023Article
- Recent Approaches to Design and Analysis of Electrical Impedance Systems for Single Cells Using Machine Learning.Sensors (Basel, Switzerland) · 2023Review
- Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems.Micromachines · 2023Review
- A low-cost, label-free microfluidic scanning flow cytometer for high-accuracy quantification of size and refractive index of particles.Lab on a chip · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- A Review of Advanced Multifunctional Magnetic Nanostructures for Cancer Diagnosis and Therapy Integrated into an Artificial Intelligence Approach.Pharmaceutics · 2023Review
- Single-Cell Classification Based on Population Nucleus Size Combining Microwave Impedance Spectroscopy and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- Advances in currentFrontiers in bioengineering and biotechnology · 2023Review
- piRT-IFC: Physics-informed real-time impedance flow cytometry for the characterization of cellular intrinsic electrical properties.Microsystems & nanoengineering · 2023Article
- Microfluidic Systems for Blood and Blood Cell Characterization.Biosensors · 2022Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Microfluidic applications such as active particle sorting or selective enrichment require particle classification techniques that are capable of working in real time. In this paper, we explore the use of neural networks for fast label-free particle characterization during microfluidic impedance cytometry. A recurrent neural network is designed to process data from a novel impedance chip layout for enabling real-time multiparametric analysis of the measured impedance data streams. As demonstrated with both synthetic and experimental datasets, the trained network is able to characterize with good accuracy size, velocity, and cross-sectional position of beads, red blood cells, and yeasts, with a unitary prediction time of 0.4 ms. The proposed approach can be extended to other device designs and cell types for electrical parameter extraction. This combination of microfluidic impedance cytometry and machine learning can serve as a stepping stone to real-time single-cell analysis and sorting. Graphical Abstract.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.