Deep learning analysis of endometrial histology as a promising tool to predict the chance of pregnancy after frozen embryo transfers.

Journal: Journal of assisted reproduction and genetics
PMID: 36862259

Abstract

PURPOSE: Endometrial histology on hematoxylin and eosin (H&E)-stained preparations provides information associated with receptivity. However, traditional histological examination by Noyes' dating method is of limited value as it is prone to subjectivity and is not well correlated with fertility status or pregnancy outcome. This study aims to mitigate the weaknesses of Noyes' dating by analyzing endometrial histology through deep learning (DL) algorithm to predict the chance of pregnancy.

Authors

  • Tiantian Li
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, People's Republic of China.
  • Renjie Liao
  • Crystal Chan
    Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, ON, Canada.
  • Ellen M Greenblatt
    Lunenfeld-Tanenbaum Research Institute (LTRI), Sinai Health System, Toronto, ON, Canada. ellen.greenblatt@sinaihealth.ca.

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