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

Rallou Perroti, Abraham Pouliakis, Niki Margari, Eleni Panopoulou, Efrossyni Karakitsou, Dimitra Iliopoulou, Ioannis Panayiotides and Dimitrios Dionysios Koutsouris
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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

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2018, vol. 7, issue 3, 37-56

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.

Date: 2018
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