Artificial intelligence deep learning model assessment of leukocyte counts and proliferation in endometrium from women with and without polycystic ovary syndrome.

Journal: F&S science
PMID: 35560015

Abstract

OBJECTIVE: To study whether artificial intelligence (AI) technology can be used to discern quantitative differences in endometrial immune cells between cycle phases and between samples from women with polycystic ovary syndrome (PCOS) and non-PCOS controls. Only a few studies have analyzed endometrial histology using AI technology, and especially, studies of the PCOS endometrium are lacking, partly because of the technically challenging analysis and unavailability of well-phenotyped samples. Novel AI technologies can overcome this problem.

Authors

  • Marika H Kangasniemi
    Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Elina K Komsi
    Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Henna-Riikka Rossi
    Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Annikki Liakka
    Department of Pathology, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Masuma Khatun
    Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Joseph C Chen
    Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Francisco, California.
  • Mariana Paulson
    Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden; Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, Stockholm, Sweden.
  • Angelica L Hirschberg
    Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden; Department of Gynecology and Reproductive Medicine, Karolinska University Hospital, Stockholm, Sweden.
  • Riikka K Arffman
    Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Terhi T Piltonen
    Department of Obstetrics and Gynecology, PEDEGO Research Unit, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland. Electronic address: terhi.piltonen@oulu.fi.

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