Automated Evaluation of Human Embryo Blastulation and Implantation Potential using Deep-Learning.

Yoav Kan-Tor, Nir Zabari, Ity Erlich, Adi Szeskin, Tamar Amitai, Dganit Richter, Yuval Or, Zeev Shoham, Arye Hurwitz, Iris Har-Vardi, Matan Gavish, Assaf Ben-Meir, Amnon Buxboim

Research output: Contribution to journalArticlepeer-review


In in vitro fertilization (IVF) treatments, early identification of embryos with high implantation potential is required for shortening time to pregnancy while avoiding clinical complications to the newborn and the mother caused by multiple pregnancies. Current classification tools are based on morphological and morphokinetic parameters that are manually annotated using time-lapse video files. However, manual annotation introduces interobserver and intraobserver variability and provides a discrete representation of preimplantation development while ignoring dynamic features that are associated with embryo quality. A fully automated and standardized classifiers are developed by training deep neural networks directly on the raw video files of >6200 blastulation-labeled and >5500 implantation-labeled embryos. Prediction of embryo implantation is more accurate than the current state-of-the-art morphokientic classifier. Embryo classification improves with video length where the most predictive images show only partial association with morphological features. Deep learning substitute to human evaluation of embryo developmental competence thus contributes to implementing single embryo transfer methodology.
Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalAdvanced intelligent systems
Issue number10
StatePublished - 2020


  • automated embryo classification
  • deep learning
  • embryo transfers
  • in vitro fertilization


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