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Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer
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Author(s): Abraham Pouliakis (National and Kapodistrian University of Athens, Greece), Niki Margari (National and Kapodistrian University of Athens, Greece), Effrosyni Karakitsou (University of Barcelona, Spain), Evangelia Alamanou (Tzaneio Hospital, Greece), Nikolaos Koureas (St. Savas Hospital, Greece), George Chrelias (National and Kapodistrian University of Athens, Greece), Vasileios Sioulas (National and Kapodistrian University of Athens, Greece), Asimakis Pappas (MHTERA Maternity Hospital, Greece), Charalambos Chrelias (National and Kapodistrian University of Athens, Greece), Emmanouil G. Terzakis (St. Savas Hospital, Greece), Vasileia Damaskou (National and Kapodistrian University of Athens, Greece), Ioannis G. Panayiotides (National and Kapodistrian University of Athens, Greece)and Petros Karakitsos (National and Kapodistrian University of Athens, Greece)
Copyright: 2020
Pages: 14
Source title:
Natural Language Processing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0951-7.ch014
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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.
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