Advertisement
Review Article| Volume 59, ISSUE 6, 102986, December 2020

Artificial intelligence meets hematology

  • Lars Kaestner
    Correspondence
    Correspondence to: University Campus - Building E2_6, Room 3.17, Saarland University, 66123, Saarbrücken, Germany.
    Affiliations
    Theoretical Medicine and Biosciences, Medical Faculty, Saarland University, 66424, Homburg, Germany

    Dynamics of Fluids, Experimental Physics, Saarland University, 66123, Saarbruecken, Germany
    Search for articles by this author
Published:October 31, 2020DOI:https://doi.org/10.1016/j.transci.2020.102986

      Abstract

      The application of artificial intelligence (AI) in hematology it not new at all. However, it increasingly becomes part of the measurement of hematological parameters and subsequently also influences decision making. Here some examples are provided where well established parameters could be exploited better, if data are not reduced to single values but instead the entire data generation process is considered. Furthermore applications of artificial neural networks (ANN), point of care (PoC) devices and the internet of things (IoT) are discussed. Beside all the technical advancements human judgement will remain the last decision.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Transfusion and Apheresis Science
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Zini G.
        Artificial intelligence in hematology.
        Hematology. 2005; 10: 393-400
        • Reichel F.
        • Mauer J.
        • Ahsan Nawaz A.
        • Gompper G.
        • Guck J.
        • Fedosov D.A.
        High-throughput microfluidic characterization of erythrocyte shapes and mechanical variability.
        Biophys J. 2019; 117: 14-24
        • Makhro A.
        • Hegemann I.
        • Seiler E.
        • Simionato G.
        • Claveria V.
        • Bogdanov N.
        • et al.
        A pilot clinical phase II trial MemSID: acute and durable changes of red blood cells of sickle cell disease patients on memantine treatment.
        eJHaem. 2020; 1: 23-34
        • Kihm A.
        • Kaestner L.
        • Wagner C.
        • Quint S.
        Classification of red blood cell shapes in flow using outlier tolerant machine learning.
        PLoS Comput Biol. 2018; 14e1006278
        • Simionato G.
        • Hinkelmann K.
        • Chachanidze R.
        • Bianchi P.
        • Fermo E.
        • van Wijk R.
        • et al.
        Artificial neural networks for 3D cell shape recognition from confocal images.
        arXiv. 2005; (08040)
        • Darras A.
        • Peikert K.
        • Rabe A.
        • Yaya F.
        • Simionato G.
        • John T.
        Erythrocyte sedimentation rate as a new diagnostic biomarker for neuroacanthocytosis syndromes.
        medRxiv. 2020; (09.01.20185041)
        • Zaninoni A.
        • Fermo E.
        • Vercellati C.
        • Consonni D.
        • Marcello A.P.
        • Zanella A.
        Use of laser assisted optical rotational cell analyzer (LoRRca MaxSis) in the diagnosis of RBC membrane disorders, enzyme defects, and congenital dyserythropoietic anemias: a monocentric study on 202 patients.
        Front Physiol. 2018; 9: 451
        • Fermo E.
        • Bogdanova A.
        • Petkova-Kirova P.
        • Zaninoni A.
        • Marcello A.P.
        • Makhro A.
        • et al.
        ‘Gardos Channelopathy’: a variant of hereditary Stomatocytosis with complex molecular regulation.
        Sci Rep. 2017; 7: 1744
        • Hertz L.
        • Huisjes R.
        • Llaudet-Planas E.
        • Pektova-Kirova P.
        • Makhro A.
        • Danielczok J.G.
        • et al.
        Is Increased Intracellular Calcium in Red Blood Cells a Common Component in the Molecular Mechanism Causing Anemia?.
        Front Physiol. 2017; 8: 673
        • Rotordam M.G.
        • Fermo E.
        • Becker N.
        • Barcellini W.
        • Brüggemann A.
        • Fertig N.
        • et al.
        A novel gain-of-function mutation of Piezo1 is functionally affirmed in red blood cells by high-throughput patch clamp.
        Haematologica. 2019; 104: e181
        • Petkova-Kirova P.
        • Hertz L.
        • Danielczok J.
        • Huisjes R.
        • Makhro A.
        • Bogdanova A.
        • et al.
        Red blood cell membrane conductance in hereditary haemolytic anaemias.
        Front Physiol. 2019; 10: 386
        • Bogdanova A.
        • Kaestner L.
        Early-career scientists’ guide to the red blood cell - don’t panic!.
        Front Physiol. 2020; 11: 588
        • Kaestner L.
        • Bianchi P.
        Trends in the development of diagnostic tools for red blood cell related diseases and anaemias.
        Front Physiol. 2020; 11: 387