: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.

Journal: Frontiers in microbiology
Published Date: Nov 24, 2023

Abstract

INTRODUCTION: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it.

Authors

  • Carles Rubio Maturana
    Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
  • Allisson Dantas de Oliveira
    Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain.
  • Sergi Nadal
    Database Technologies and Information Group, Service and Information Systems Engineering Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
  • Francesc Zarzuela Serrat
    Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
  • Elena Sulleiro
    Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
  • Edurne Ruiz
    Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
  • Besim Bilalli
    Database Technologies and Information Group, Service and Information Systems Engineering Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
  • Anna Veiga
    Probitas Foundation, Barcelona, Spain.
  • Mateu Espasa
    Department of Microbiology and Genetics, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
  • Alberto Abelló
    Database Technologies and Information Group, Service and Information Systems Engineering Department, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
  • Tomàs Pumarola Suñé
    Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
  • Marta Segú
    Futbol Club Barcelona Foundation, Barcelona, Spain.
  • Daniel López-Codina
    Computational Biology and Complex Systems Group, Physics Department, Universitat Politècnica de Catalunya (UPC), Castelldefels, Spain.
  • Elisa Sayrol Clols
    Tecnocampus, Universitat Pompeu Fabra, Mataró, Spain.
  • Joan Joseph-Munné
    Microbiology Department, Vall d'Hebron University Hospital, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.

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