Your browser doesn't support javascript.
loading
Automation in Flow Cytometry.
Insuasti-Beltran, Giovanni; Al-Attar, Ahmad.
Afiliación
  • Insuasti-Beltran G; Wake Forest University, 1 Medical Center Boulevard, Winston-Salem, NC 27157, USA. Electronic address: ginsuast@wakehealth.edu.
  • Al-Attar A; Flow Cytometry Laboratory, University of Louisville Health, 529 S Jackson Street, Louisville, KY 40202, USA.
Clin Lab Med ; 44(3): 455-463, 2024 Sep.
Article en En | MEDLINE | ID: mdl-39089751
ABSTRACT
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.
Asunto(s)
Palabras clave

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Citometría de Flujo Idioma: En Revista: Clin Lab Med Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Citometría de Flujo Idioma: En Revista: Clin Lab Med Año: 2024 Tipo del documento: Article