Recommendations for using artificial intelligence in clinical flow cytometry.

Journal: Cytometry. Part B, Clinical cytometry
Published Date: Feb 26, 2024

Abstract

Flow cytometry is a key clinical tool in the diagnosis of many hematologic malignancies and traditionally requires close inspection of digital data by hematopathologists with expert domain knowledge. Advances in artificial intelligence (AI) are transferable to flow cytometry and have the potential to improve efficiency and prioritization of cases, reduce errors, and highlight fundamental, previously unrecognized associations with underlying biological processes. As a multidisciplinary group of stakeholders, we review a range of critical considerations for appropriately applying AI to clinical flow cytometry, including use case identification, low and high risk use cases, validation, revalidation, computational considerations, and the present regulatory frameworks surrounding AI in clinical medicine. In particular, we provide practical guidance for the development, implementation, and suggestions for potential regulation of AI-based methods in the clinical flow cytometry laboratory. We expect these recommendations to be a helpful initial framework of reference, which will also require additional updates as the field matures.

Authors

  • David P Ng
    Department of Pathology, University of Utah, Salt Lake City.
  • Paul D Simonson
    Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Attila Tárnok
    Department of Therapy Validation, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Leipzig, Germany.
  • Fabienne Lucas
    Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.
  • Wolfgang Kern
    MLL Munich Leukemia Laboratory, Munich, Germany.
  • Nina Rolf
    BC Children's Hospital Research Institute, University of British Columbia, Vancouver, British Columbia, Canada.
  • Goce Bogdanoski
    Clinical Development & Operations Quality, R&D Quality, Bristol Myers Squibb, Princeton, New Jersey, USA.
  • Cherie Green
    Translational Science, Ozette Technologies, Seattle, Washington, USA.
  • Ryan R Brinkman
    Molecular Biology and Biochemistry Department, Simon Fraser University, Burnaby, BC V5A 1S6, Canada, Terry Fox Laboratory, British Columbia Cancer Agency, Vancouver, BC V5Z 1L3, Canada, Department of Neurology, University at Buffalo School of Medicine and Biomedical Sciences, Buffalo, NY 14203, USA, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA 94305, USA, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892, USA, Center for Human Immunology, Autoimmunity and Inflammation, National Institutes of Health, Bethesda, MD 20892, USA, School of Dental Medicine, University at Buffalo, NY 14214-8006, USA, J. Craig Venter Institute, La Jolla, CA 92037, USA, Department of Pathology, University of California, San Diego, CA 92093, USA.
  • Kamila Czechowska
    Metafora Biosystems, PARIS, France.

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