Application of Deep Learning in Optimal Embryo Selection of In Vitro Fertilization

doi:10.12280/gjszjk.20220553

Abstract

Abstract:

The selection of transferred embryo is one of the most important factors in achieving successful pregnancy of in vitro fertilization-embryo transfer. At present, the most common method of embryo selection is visual evaluation of embryo morphology, which is highly dependent on subjective vision and personal experience of lab technicians. This method may affect the accuracy and consistency of optimal embryo selection. Recently, some studies have tried to introduce the deep learning algorithm into embryo selection. The deep learning model is developed to assess quality, and to predict outcome based on a large number of manually labeled embryo images and vedios. It has been found that the deep learning algorithm was objective, accurate, efficient and stable. This paper reviews the application of deep learning, as well as its research progress, in embryo selection, and compares deep learning with manual evaluation or classic machine learning algorithms, so as to provide a glimpse into the application value of deep learning in assisted reproduction.

Key words: Fertilization in vitro, Embryo transfer, Deep learning, Embryo selection, Embryo quality, Pregnancy outcome

HUO Wen-jie, WANG Xiao-cong, PENG Fei, QUAN Song. Application of Deep Learning in Optimal Embryo Selection of In Vitro Fertilization[J]. Journal of International Reproductive Health/Family Planning, 2023, 42(2): 135-139.

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