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Related Concept Videos

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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Recommendations for using artificial intelligence in clinical flow cytometry.

David P Ng1, Paul D Simonson2, Attila Tarnok3

  • 1Department of Pathology, University of Utah, Salt Lake City, Utah, USA.

Cytometry. Part B, Clinical Cytometry
|February 26, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) can enhance clinical flow cytometry for diagnosing blood cancers by improving efficiency and accuracy. This review provides guidance on developing, validating, and regulating AI tools for the flow cytometry lab.

Keywords:
artificial intelligenceclinical laboratorydevelopmentflow cytometryimplementationmachine learningmultidisciplinaryperformanceregulationsstakeholdersvalidation

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Area of Science:

  • Clinical Hematology
  • Medical Informatics
  • Computational Biology

Background:

  • Flow cytometry is essential for diagnosing hematologic malignancies.
  • Current methods rely on expert interpretation of complex data.
  • Artificial intelligence (AI) offers potential advancements in data analysis and case prioritization.

Purpose of the Study:

  • To review critical considerations for applying AI in clinical flow cytometry.
  • To provide practical guidance for AI development and implementation.
  • To suggest regulatory frameworks for AI in clinical laboratories.

Main Methods:

  • Multidisciplinary stakeholder review of AI applications in flow cytometry.
  • Analysis of use case identification, risk assessment, validation, and computational factors.
  • Examination of current regulatory landscapes for AI in medicine.

Main Results:

  • AI has the potential to improve efficiency, reduce errors, and uncover new biological insights in flow cytometry.
  • Key considerations include appropriate use case selection, rigorous validation, and computational infrastructure.
  • Existing regulatory frameworks need adaptation for AI in clinical settings.

Conclusions:

  • AI integration in clinical flow cytometry requires careful planning and validation.
  • Practical guidance and regulatory suggestions are provided for AI development and implementation.
  • Ongoing updates and collaboration are necessary as AI technology in this field matures.