Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases.

Journal: Pathology, research and practice
Published Date:

Abstract

Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic examination, a time-consuming process requiring skilled staff. Thus, artificial intelligence (AI) has been exploited for identification of microorganisms. A systematic search was carried out using electronic databases looking for studies dealing with the application of AI to pathology microbiology specimens. Of 4596 retrieved articles, 110 were included. The main applications of AI regarded malaria (54 studies), bacteria (28), nematodes (14), and other protozoa (11). Most publications examined cytological material (95, 86%), mainly analyzing images acquired through microscope cameras (65, 59%) or coupled with smartphones (16, 15%). Various deep-learning strategies were used for the analysis of digital images, achieving highly satisfactory results. The published evidence suggests that AI can be reliably utilized for assisting pathologists in the detection of microorganisms. Further technologic improvement and availability of datasets for training AI-based algorithms would help expand this field and widen its adoption, especially for developing countries.

Authors

  • Stefano Marletta
    Department of Diagnostics and Public Health, University and Hospital Trust of Verona, Verona, Italy.
  • Vincenzo L'Imperio
    Department of Medicine and Surgery, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, Monza, Italy.
  • Albino Eccher
    Department of Pathology and Diagnostics, University and Hospital Trust of Verona, P.le Stefani n. 1, 37126, Verona, Italy. albino.eccher@aovr.veneto.it.
  • Pietro Antonini
    Department of Diagnostic and Public Health, Section of Pathology, University of Verona, Verona, Italy.
  • Nicola Santonicco
    Department of Pathology and Diagnostics, Section of Pathology, University Hospital of Verona, Verona, Italy.
  • Ilaria Girolami
    Division of Pathology, Central Hospital Bolzano, Bolzano, Italy.
  • Angelo Paolo Dei Tos
    Surgical Pathology & Cytopathology Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy.
  • Marta Sbaraglia
    Surgical Pathology & Cytopathology Unit, Department of Medicine - DIMED, University of Padua, Padua, Italy.
  • Fabio Pagni
    Department of Medicine and Surgery, ASST Monza, San Gerardo Hospital, University of Milano-Bicocca, Monza, Italy.
  • Matteo Brunelli
    Department of Pathology, University of Verona, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy.
  • Andrea Marino
    Unit of Infectious Diseases, Department of Clinical and Experimental Medicine, Azienda Ospedaliera di Rilievo Nazionale e di Alta Specializzazione (ARNAS), Garibaldi Hospital, University of Catania, Catania, Italy.
  • Aldo Scarpa
    Department of Diagnostics and Public Health, University of Verona, Verona, Italy; ARC-Net Research Centre, University of Verona, Verona, Italy.
  • Enrico Munari
    Pathology Unit, Department of Molecular and Translational Medicine, Spedali Civili-University of Brescia, Brescia, Italy.
  • Nicola Fusco
    Biobank for Translational and Digital Medicine Unit, Division of Pathology, IEO, European Institute of Oncology IRCCS, University of Milan, Milan, 20141, Italy.
  • Liron Pantanowitz
    Department of Pathology, University of Michigan, Ann Arbor, MI, USA.