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Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies: Discriminating Endometrial Cells and Lesions

Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies: Discriminating Endometrial Cells and Lesions

Abraham Pouliakis, Niki Margari, Effrosyni Karakitsou, George Valasoulis, Nektarios Koufopoulos, Nikolaos Koureas, Evangelia Alamanou, Vassileios Pergialiotis, Vasileia Damaskou, Ioannis G. Panayiotides
Copyright: © 2019 |Volume: 8 |Issue: 4 |Pages: 17
ISSN: 2160-9551|EISSN: 2160-956X|EISBN13: 9781522567875|DOI: 10.4018/IJRQEH.2019100102
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MLA

Pouliakis, Abraham, et al. "Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies: Discriminating Endometrial Cells and Lesions." IJRQEH vol.8, no.4 2019: pp.38-54. http://doi.org/10.4018/IJRQEH.2019100102

APA

Pouliakis, A., Margari, N., Karakitsou, E., Valasoulis, G., Koufopoulos, N., Koureas, N., Alamanou, E., Pergialiotis, V., Damaskou, V., & Panayiotides, I. G. (2019). Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies: Discriminating Endometrial Cells and Lesions. International Journal of Reliable and Quality E-Healthcare (IJRQEH), 8(4), 38-54. http://doi.org/10.4018/IJRQEH.2019100102

Chicago

Pouliakis, Abraham, et al. "Artificial Intelligence via Competitive Learning and Image Analysis for Endometrial Malignancies: Discriminating Endometrial Cells and Lesions," International Journal of Reliable and Quality E-Healthcare (IJRQEH) 8, no.4: 38-54. http://doi.org/10.4018/IJRQEH.2019100102

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Abstract

Objective of this study is to investigate the potential of an artificial intelligence (AI) technique, based on competitive learning, for the discrimination of benign from malignant endometrial nuclei and lesions. For this purpose, 416 liquid-based cytological smears with histological confirmation were collected, each smear corresponded to one patient. From each smear was extracted nuclear morphometric features by the application of an image analysis system. Subsequently nuclei measurement from 50% of the cases were used to train the AI system to classify each individual nucleus as benign or malignant. The remaining measurement, from the unused 50% of the cases, were used for AI system performance evaluation. Based on the results of nucleus classification the patients were discriminated as having benign or malignant disease by a secondary subsystem specifically trained for this purpose. Based on the results it was conclude that AI based computerized systems have the potential for the classification of both endometrial nuclei and lesions.

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