Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders.

Journal: Pathology
Published Date: Apr 1, 2021

Abstract

Advances in digital pathology have allowed a number of opportunities such as decision support using artificial intelligence (AI). The application of AI to digital pathology data shows promise as an aid for pathologists in the diagnosis of haematological disorders. AI-based applications have embraced benign haematology, diagnosing leukaemia and lymphoma, as well as ancillary testing modalities including flow cytometry. In this review, we highlight the progress made to date in machine learning applications in haematopathology, summarise important studies in this field, and highlight key limitations. We further present our outlook on the future direction and trends for AI to support diagnostic decisions in haematopathology.

Authors

  • Ahmad Nanaa
    Division of Hematology, Mayo Clinic, Rochester, MN, USA.
  • Zeynettin Akkus
    From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
  • Winston Y Lee
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Liron Pantanowitz
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
  • Mohamed E Salama
    Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN, USA.

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