Automation in Flow Cytometry.

Artificial intelligence Automation Flow cytometry Sample handling Throughput Turnaround time

Journal

Clinics in laboratory medicine
ISSN: 1557-9832
Titre abrégé: Clin Lab Med
Pays: United States
ID NLM: 8100174

Informations de publication

Date de publication:
Sep 2024
Historique:
medline: 2 8 2024
pubmed: 2 8 2024
entrez: 1 8 2024
Statut: ppublish

Résumé

Automation in clinical flow cytometry has the potential to revolutionize the field by improving processes and enhancing efficiency and accuracy. Integrating advanced robotics and artificial intelligence, these technologies can streamline sample preparation, data acquisition, and analysis. Automated sample handling reduces human error and increases throughput, allowing laboratories to handle larger volumes with consistent precision. Intelligent algorithms contribute to rapid data interpretation, aiding in the identification of cellular markers for disease diagnosis and monitoring. This automation not only accelerates turnaround times but also ensures reproducibility, making clinical flow cytometry a reliable tool in the realm of personalized medicine and diagnostics.

Identifiants

pubmed: 39089751
pii: S0272-2712(24)00018-0
doi: 10.1016/j.cll.2024.04.007
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

455-463

Informations de copyright

Copyright © 2024 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Disclosure The authors have nothing to disclose.

Auteurs

Giovanni Insuasti-Beltran (G)

Wake Forest University, 1 Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address: ginsuast@wakehealth.edu.

Ahmad Al-Attar (A)

Flow Cytometry Laboratory, University of Louisville Health, 529 S Jackson Street, Louisville, KY 40202, USA.

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Classifications MeSH