An Artificial Intelligence Approach for the Detection of Cervical Abnormalities-Reference-Cited by-同舟云学术

An Artificial Intelligence Approach for the Detection of Cervical Abnormalities

Published:2022-07-01 Issue: Volume: Page:267-288
ISSN:
Container-title:Research Anthology on Medical Informatics in Breast and Cervical Cancer
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Author:

Salamalekis Evangelos¹,Pouliakis Abraham²^ORCID,Margari Niki³^ORCID,Kottaridi Christine⁴,Spathis Aris⁴^ORCID,Karakitsou Effrosyni⁵,Gouloumi Alina-Roxani⁴,Leventakou Danai⁴^ORCID,Chrelias George⁶,Valasoulis George⁷^ORCID,Nasioutziki Maria⁸,Kyrgiou Maria⁹,Dinas Konstantinos¹⁰,Panayiotides Ioannis G⁴,Paraskevaidis Evangelos¹¹,Chrelias Charalampos⁶

Affiliation:

1. Department of Cytopathology, Evangelismos Hospital, Paphos, Greece

2. 2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece

3. Private Cytopathology Laboratory, Marousi, Greece

4. 2nd Department of Pathology, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece

5. Department of Biology, University of Barcelona, Barcelona, Spain

6. 3rd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, Greece

7. Department of Obstetrics and Gynaecology, Health Center of Larisa, Larisa, Greece

8. Molecular Cytopathology Laboratory, 2nd Obstetrics and Gynecology Department, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece

9. Department of Surgery and Cancer, Imperial College London, London, UK

10. 2nd Obstetrics and Gynecology Department, Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece

11. Department of Obstetrics and Gynecology, University Hospital of Ioannina, Ioannina, Greece

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.

Publisher

IGI Global

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