Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer
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 cel...
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| Veröffentlicht in: | International journal of reliable and quality e-healthcare 2018-04, Vol.7 (2), p.37-50 |
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| creator | Pouliakis, Abraham Margari, Niki Karakitsou, Effrosyni Alamanou, Evangelia Koureas, Nikolaos Chrelias, George Sioulas, Vasileios Pappas, Asimakis Chrelias, Charalambos Terzakis, Emmanouil G Damaskou, Vasileia Panayiotides, Ioannis G Karakitsos, Petros |
| description | 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. |
| doi_str_mv | 10.4018/IJRQEH.2018040103 |
| format | Article |
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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.</description><identifier>ISSN: 2160-9551</identifier><identifier>EISSN: 2160-956X</identifier><identifier>DOI: 10.4018/IJRQEH.2018040103</identifier><language>eng</language><publisher>Hershey: IGI Global</publisher><subject>Algorithms ; Artificial intelligence ; Cancer ; Classification ; Endometrial cancer ; Image analysis ; Learning vector quantization networks ; Lesions ; Neural networks ; Nuclei (cytology) ; Performance indices ; Sensitivity</subject><ispartof>International journal of reliable and quality e-healthcare, 2018-04, Vol.7 (2), p.37-50</ispartof><rights>COPYRIGHT 2018 IGI Global</rights><rights>Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2817-405ae4f1ee69de47870e7cca603dfd6d5545f6935c43713c5b0ccd527fb9dff43</cites><orcidid>0000-0002-0074-3619 ; 0000-0002-6584-8833</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2932382177?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>315,781,785,21392,21393,21394,21395,23260,27928,27929,33534,33707,33748,34009,34318,43663,43791,43809,43957,44071,64389,64393,72473</link.rule.ids></links><search><creatorcontrib>Pouliakis, Abraham</creatorcontrib><creatorcontrib>Margari, Niki</creatorcontrib><creatorcontrib>Karakitsou, Effrosyni</creatorcontrib><creatorcontrib>Alamanou, Evangelia</creatorcontrib><creatorcontrib>Koureas, Nikolaos</creatorcontrib><creatorcontrib>Chrelias, George</creatorcontrib><creatorcontrib>Sioulas, Vasileios</creatorcontrib><creatorcontrib>Pappas, Asimakis</creatorcontrib><creatorcontrib>Chrelias, Charalambos</creatorcontrib><creatorcontrib>Terzakis, Emmanouil G</creatorcontrib><creatorcontrib>Damaskou, Vasileia</creatorcontrib><creatorcontrib>Panayiotides, Ioannis G</creatorcontrib><creatorcontrib>Karakitsos, Petros</creatorcontrib><title>Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer</title><title>International journal of reliable and quality e-healthcare</title><description>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.</description><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Cancer</subject><subject>Classification</subject><subject>Endometrial cancer</subject><subject>Image analysis</subject><subject>Learning vector quantization networks</subject><subject>Lesions</subject><subject>Neural networks</subject><subject>Nuclei (cytology)</subject><subject>Performance 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Abraham</creator><creator>Margari, Niki</creator><creator>Karakitsou, Effrosyni</creator><creator>Alamanou, Evangelia</creator><creator>Koureas, Nikolaos</creator><creator>Chrelias, George</creator><creator>Sioulas, Vasileios</creator><creator>Pappas, Asimakis</creator><creator>Chrelias, Charalambos</creator><creator>Terzakis, Emmanouil G</creator><creator>Damaskou, Vasileia</creator><creator>Panayiotides, Ioannis G</creator><creator>Karakitsos, Petros</creator><general>IGI 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Petros</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer</atitle><jtitle>International journal of reliable and quality e-healthcare</jtitle><date>2018-04-01</date><risdate>2018</risdate><volume>7</volume><issue>2</issue><spage>37</spage><epage>50</epage><pages>37-50</pages><issn>2160-9551</issn><eissn>2160-956X</eissn><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.</abstract><cop>Hershey</cop><pub>IGI Global</pub><doi>10.4018/IJRQEH.2018040103</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-0074-3619</orcidid><orcidid>https://orcid.org/0000-0002-6584-8833</orcidid></addata></record> |
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| subjects | Algorithms Artificial intelligence Cancer Classification Endometrial cancer Image analysis Learning vector quantization networks Lesions Neural networks Nuclei (cytology) Performance indices Sensitivity |
| title | Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer |
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