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CytoNet, a Versatile Web-Based System for Accessing Advisory Cytology Services: Application of Artificial Intelligence
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Author(s): Rallou Perroti (National Technical University of Athens, Greece), Abraham Pouliakis (National and Kapodistrian University of Athens, Greece), Niki Margari (National and Kapodistrian University of Athens, Greece), Eleni Panopoulou (National and Kapodistrian University of Athens, Greece), Efrossyni Karakitsou (University of Barcelona, Spain), Dimitra Iliopoulou (National Technical University of Athens, Greece), Ioannis Panayiotides (National and Kapodistrian University of Athens, Greece)and Dimitrios Dionysios Koutsouris (National Technical University of Athens, Greece)
Copyright: 2019
Pages: 17
Source title:
Biotechnology: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-8903-7.ch043
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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.
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