Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer

Abraham Pouliakis, Niki Margari, Effrosyni Karakitsou, Evangelia Alamanou, Nikolaos Koureas, George Chrelias, Vasileios Sioulas, Asimakis Pappas, Charalambos Chrelias, Emmanouil G. Terzakis, Vasileia Damaskou, Ioannis G. Panayiotides and Petros Karakitsos
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Abraham Pouliakis: 2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece
Niki Margari: Department of Cytopathology, National and Kapodistrian University of Athens, Athens, Greece
Effrosyni Karakitsou: Department of Biology, University of Barcelona, Barcelona, Spain
Evangelia Alamanou: Department of Obstetrics and Gynecology, Tzaneio Hospital, Athens, Greece
Nikolaos Koureas: 2nd Department of Gynecology, St. Savas Hospital, Athens, Greece
George Chrelias: 3rd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, Greece
Vasileios Sioulas: 3rd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, Greece
Asimakis Pappas: MHTERA Maternity Hospital,Obstetrics and Gynecology, Athens, Greece
Charalambos Chrelias: 3rd Department of Obstetrics and Gynecology, National and Kapodistrian University of Athens, Athens, Greece
Emmanouil G. Terzakis: Department of Gynecology, St. Savas Hospital, Athens, Greece
Vasileia Damaskou: 2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece
Ioannis G. Panayiotides: 2nd Department of Pathology, National and Kapodistrian University of Athens, Athens, Greece
Petros Karakitsos: Department of Cytopathology, National and Kapodistrian University of Athens, Athens, Greece

International Journal of Reliable and Quality E-Healthcare (IJRQEH), 2018, vol. 7, issue 2, 37-50

Abstract: Aim of this article is to investigate the potential of Artificial Intelligence (AI) in the discrimination between benign and malignant endometrial nuclei and lesions. For this purpose, 416 histologically confirmed liquid-based cytological smears were collected and morphometric characteristics of cell nuclei were measured via image analysis. Then, 50% of the cases were used to train an AI system, specifically a learning vector quantization (LVQ) neural network. As a result, cell nuclei were classified as benign or malignant. Data from the remaining 50% of the cases were used to evaluate the AI system performance. By nucleic classification, an algorithm for the classification of individual patients was constructed, and performance indices on patient classification were calculated. The sensitivity for the classification of nuclei was 77.95%, and the specificity was 73.93%. For the classification of individual patients, the sensitivity was 90.70% and the specificity 82.79%. These results indicate that an AI system can have an important role in endometrial lesions classification.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jrqeh0:v:7:y:2018:i:2:p:37-50

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