Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018

J Assist Reprod Genet. 2019 Apr;36(4):591-600. doi: 10.1007/s10815-019-01408-x. Epub 2019 Jan 28.


Sixteen artificial intelligence (AI) and machine learning (ML) approaches were reported at the 2018 annual congresses of the American Society for Reproductive Biology (9) and European Society for Human Reproduction and Embryology (7). Nearly every aspect of patient care was investigated, including sperm morphology, sperm identification, identification of empty or oocyte containing follicles, predicting embryo cell stages, predicting blastocyst formation from oocytes, assessing human blastocyst quality, predicting live birth from blastocysts, improving embryo selection, and for developing optimal IVF stimulation protocols. This represents a substantial increase in reports over 2017, where just one abstract each was reported at ASRM (AI) and ESHRE (ML). Our analysis reveals wide variability in how AI and ML methods are described (from not at all or very generic to fully describing the architectural framework) and large variability on accepted dataset sizes (from just 3 patients with 16 follicles in the smallest dataset to 661,060 images of 11,898 human embryos in one of the largest). AI and ML are clearly burgeoning methodologies in human reproduction and embryology and would benefit from early application of reporting standards.

Keywords: ASHRE; ASRM; Artificial intelligence; Embryology; Human reproduction; Machine learning.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence / trends*
  • Blastocyst / cytology
  • Embryonic Development*
  • Female
  • Fertilization in Vitro / trends
  • Humans
  • Machine Learning / trends*
  • Oocytes / growth & development
  • Pregnancy
  • Pregnancy Outcome
  • Reproductive Techniques, Assisted / trends*