IDEAS home Printed from https://ideas.repec.org/a/igg/jrqeh0/v7y2018i2p37-50.html
   My bibliography  Save this article

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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Abraham Pouliakis & Niki Margari & Effrosyni Karakitsou & Evangelia Alamanou & Nikolaos Koureas & George Chrelias & Vasileios Sioulas & Asimakis Pappas & Charalambos Chrelias & Emmanouil G. Terzakis &, 2018. "Artificial Intelligence for the Identification of Endometrial Malignancies: Application of the Learning Vector Quantizer," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 7(2), pages 37-50, April.
    • Handle: RePEc:igg:jrqeh0:v:7:y:2018:i:2:p:37-50
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJRQEH.2018040103
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jrqeh0:v:7:y:2018:i:2:p:37-50. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.