An Artificial Intelligence Approach for the Detection of Cervical Abnormalities: Application of the Self Organizing Map

Evangelos Salamalekis, Abraham Pouliakis, Niki Margari, Christine Kottaridi, Aris Spathis, Effrosyni Karakitsou, Alina-Roxani Gouloumi, Danai Leventakou, George Chrelias, George Valasoulis, Maria Nasioutziki, Maria Kyrgiou, Konstantinos Dinas, Ioannis G. Panayiotides, Evangelos Paraskevaidis and Charalampos Chrelias
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Evangelos Salamalekis: Department of Cytopathology, Evangelismos Hospital, Paphos, Greece
Abraham Pouliakis: 2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece
Niki Margari: Private Cytopathology Laboratory, Marousi, Greece
Christine Kottaridi: 2nd Department of Pathology, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
Aris Spathis: 2nd Department of Pathology, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
Effrosyni Karakitsou: Department of Biology, University of Barcelona, Barcelona, Spain
Alina-Roxani Gouloumi: 2nd Department of Pathology, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
Danai Leventakou: 2nd Department of Pathology, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
George Chrelias: 3rd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, Greece
George Valasoulis: Department of Obstetrics and Gynaecology, Health Center of Larisa, Larisa, Greece
Maria Nasioutziki: Molecular Cytopathology Laboratory, 2nd Obstetrics and Gynecology Department, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece
Maria Kyrgiou: Department of Surgery and Cancer, Imperial College London, London, UK
Konstantinos Dinas: 2nd Obstetrics and Gynecology Department, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece
Ioannis G. Panayiotides: 2nd Department of Pathology, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
Evangelos Paraskevaidis: Department of Obstetrics and Gynecology, University Hospital of Ioannina, Ioannina, Greece
Charalampos Chrelias: 3rd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, Greece

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2019, vol. 8, issue 2, 15-35

Abstract: Numerous ancillary techniques detecting HPV DNA or mRNA are viewed as competitors or ancillary techniques to test Papanicolaou. However, no technique is perfect because sensitivity increases at the cost of specificity. Various methods have been applied to resolve this issue by using many examination results, such as classification and regression trees and supervised artificial neural networks. In this article, 1258 cases with results from test Pap, HPV DNA, HPV mRNA, and p16 were used to evaluate the performance of the self-organizing map (SOM). An artificial neural network has three advantages: it is unsupervised, can tolerate missing data, and produces topographical maps. The results of the SOM application were encouraging and produced maps depicting the important tests.

Date: 2019
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