Deciphering impedance cytometry signals with neural networks-Reference-Cited by-同舟云学术

Deciphering impedance cytometry signals with neural networks

Published:2022 Issue:9 Volume:22 Page:1714-1722
ISSN:1473-0197
Container-title:Lab on a Chip
language:en
Short-container-title:Lab Chip

Author:

Caselli Federica¹^ORCID,Reale Riccardo²,De Ninno Adele³,Spencer Daniel⁴^ORCID,Morgan Hywel⁴,Bisegna Paolo¹^ORCID

Affiliation:

1. Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy

2. Center for Life Nano Science@Sapienza, Italian Institute of Technology (IIT), Rome, Italy

3. Italian National Research Council - Institute for Photonics and Nanotechnologies (CNR - IFN), Rome, Italy

4. School of Electronics and Computing Science, and, Institute for Life Sciences, University of Southampton, Highfield, Southampton, UK

Abstract

A successful outcome of the coupling between microfluidics and AI: neural networks tackle the signal processing challenges of single-cell microfluidic impedance cytometry.

Funder

Regione Lazio

Publisher

Royal Society of Chemistry (RSC)

Subject

Biomedical Engineering,General Chemistry,Biochemistry,Bioengineering

Link

http://pubs.rsc.org/en/content/articlepdf/2022/LC/D2LC00028H

Reference44 articles.

1. Deep Learning with Microfluidics for Biotechnology

2. AI on a chip

3. Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

4. Exploiting machine learning for bestowing intelligence to microfluidics

5. Review: imaging technologies for flow cytometry

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