Application of artificial intelligence in gynecologic malignancies: A review

  • Yuichiro Miyamoto
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Michihiro Tanikawa
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Mayuyo Uchino‐Mori
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Takayuki Iriyama
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Tetsushi Tsuruga
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Ayumi Taguchi
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Yutaka Osuga
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Kenbun Sone
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan
  • Yusuke Toyohara
    Department of Obstetrics and Gynecology, Faculty of Medicine The University of Tokyo Tokyo Japan

Bibliographic Information

Published
2021-05-10
Resource Type
journal article
Rights Information
  • http://onlinelibrary.wiley.com/termsAndConditions#vor
DOI
  • 10.1111/jog.14818
Publisher
Wiley

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<jats:title>Abstract</jats:title><jats:p>With the development of machine learning and deep learning models, artificial intelligence is now being applied to the field of medicine. In oncology, the use of artificial intelligence for the diagnostic evaluation of medical images such as radiographic images, omics analysis using genome data, and clinical information has been increasing in recent years. There have been increasing numbers of reports on the use of artificial intelligence in the field of gynecologic malignancies, and we introduce and review these studies. For cervical and endometrial cancers, the evaluation of medical images, such as colposcopy, hysteroscopy, and magnetic resonance images, using artificial intelligence is frequently reported. In ovarian cancer, many reports combine the assessment of medical images with the multi‐omics analysis of clinical and genomic data using artificial intelligence. However, few study results can be implemented in clinical practice, and further research is needed in the future.</jats:p>

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