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

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...

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Bibliographic Details
Published inInternational journal of reliable and quality e-healthcare Vol. 7; no. 3; pp. 37 - 56
Main Authors Panayiotides, Ioannis, Perroti, Rallou, Pouliakis, Abraham, Margari, Niki, Panopoulou, Eleni, Karakitsou, Efrossyni, Iliopoulou, Dimitra, Koutsouris, Dimitrios Dionysios
Format Journal Article
LanguageEnglish
Published Hershey IGI Global 01.07.2018
Subjects
Accuracy
Artificial intelligence
Biomedical engineering
Biopsy
Cellular biology
Classification
Cytology
Fuzzy logic
Laboratories
Neural networks
Pathology
Patients
Regression analysis
Statistical methods
Thyroid diseases
Ultrasonic imaging
User requirements
Greece
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Summary: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.
ISSN:2160-9551
2160-956X
DOI:10.4018/IJRQEH.2018070103