title: Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future.
abstract: OBJECTIVE
  This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.
  
  
  METHODS
  A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.
  
  
  RESULTS
  The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.
  
  
  CONCLUSIONS
  Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.
authors.affiliation: Department of Cytopathology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, Athens, Greece.
authors.forename: Abraham
authors.initials: A
authors.lastname: Pouliakis
authors.affiliation: 2nd Department of Pathology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, Athens, Greece.
authors.forename: Efrossyni
authors.initials: E
authors.lastname: Karakitsou
authors.affiliation: Department of Cytopathology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, Athens, Greece.
authors.forename: Niki
authors.initials: N
authors.lastname: Margari
authors.affiliation: Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece.
authors.forename: Panagiotis
authors.initials: P
authors.lastname: Bountris
authors.affiliation: Institute of Communication and Computer Systems, Athens, Greece.
authors.forename: Maria
authors.initials: M
authors.lastname: Haritou
authors.affiliation: 2nd Department of Pathology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, Athens, Greece.
authors.forename: John
authors.initials: J
authors.lastname: Panayiotides
authors.affiliation: Biomedical Engineering Laboratory, National Technical University of Athens, Athens, Greece.
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Koutsouris
authors.affiliation: Department of Cytopathology, National and Kapodistrian University of Athens, Medical School, Attikon University Hospital, Athens, Greece.
authors.forename: Petros
authors.initials: P
authors.lastname: Karakitsos
keywords: artificial intelligence; artificial neural networks; automation; computer-assisted diagnosis; cytology; cytopathology; decision support; neural networks; review