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CytoNet, A Versatile Web-Based System for Accessing Advisory Cytology Services: Application of Artificial Intelligence

Author

Listed:
  • Rallou Perroti

    (Biomedical Engineering Laboratory, National Technical University of Athens, Zografou, Greece)

  • Abraham Pouliakis

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Chaidari, Greece)

  • Niki Margari

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Chaidari, Greece)

  • Eleni Panopoulou

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Chaidari, Greece)

  • Efrossyni Karakitsou

    (Department of Biology, University of Barcelona, Barcelona, Spain)

  • Dimitra Iliopoulou

    (Biomedical Engineering Laboratory, National Technical University of Athens, Zografou, Greece)

  • Ioannis Panayiotides

    (2nd Department of Pathology, National and Kapodistrian University of Athens, Chaidari, Greece)

  • Dimitrios Dionysios Koutsouris

    (Biomedical Engineering Laboratory, National Technical University of Athens, Zografou, Greece)

Abstract

This article describes how the use of artificial intelligence applications as a consultation tool on a cytological laboratory's daily routine has been suggested for several decades. In addition to the use of high-resolution thyroid ultrasonography and fine-needle aspiration cytology, a further reduction of the number of unnecessary thyroidectomies can be achieved through the access to such techniques. Despite the evident advantages, artificial intelligence applications hardly ever find their way to end-users due to the specialized knowledge necessary for designing and using them, as well as the users' unfamiliarity with the required technology. The authors aimed to design an easy-to-use online platform (CytoNet) that gives access to a learning vector quantizer neural network (LVQ NN) that discriminates benign from malignant thyroid lesions to users (medical doctors) with no specialized technical background on artificial intelligence.

Suggested Citation

  • Rallou Perroti & Abraham Pouliakis & Niki Margari & Eleni Panopoulou & Efrossyni Karakitsou & Dimitra Iliopoulou & Ioannis Panayiotides & Dimitrios Dionysios Koutsouris, 2018. "CytoNet, A Versatile Web-Based System for Accessing Advisory Cytology Services: Application of Artificial Intelligence," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 7(3), pages 37-56, July.
    • Handle: RePEc:igg:jrqeh0:v:7:y:2018:i:3:p:37-56
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    Cited by:

    1. Charalampos Siristatidis & Abraham Pouliakis & Vasilios Karageorgiou & Paraskevi Vogiatzi, 2019. "Mobile Apps for Helping Patient-Users: Is It Still Far-Fetched?," Sustainability, MDPI, vol. 12(1), pages 1-7, December.

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