Editing In vitro fertilisation (section)

===Embryo selection===
{{Further|Embryo quality}}
Laboratories have developed grading methods to judge ovocyte and [[embryo]] quality. In order to optimise [[pregnancy rate]]s, there is significant evidence that a morphological scoring system is the best strategy for the selection of embryos.<ref name=Rebmann>{{cite journal | vauthors = Rebmann V, Switala M, Eue I, Grosse-Wilde H | title = Soluble HLA-G is an independent factor for the prediction of pregnancy outcome after ART: a German multi-centre study | journal = Human Reproduction | volume = 25|issue = 7 | pages = 1691–1698 | date = July 2010 | pmid = 20488801 | doi = 10.1093/humrep/deq120 | doi-access = free }}</ref> Since 2009 where the first [[time-lapse microscopy]] system for IVF was approved for clinical use, morphokinetic scoring systems has shown to improve to [[pregnancy rate]]s further.<ref name="Meseguer">{{cite journal | vauthors = Meseguer M, Rubio I, Cruz M, Basile N, Marcos J, Requena A | title = Embryo incubation and selection in a time-lapse monitoring system improves pregnancy outcome compared with a standard incubator: a retrospective cohort study | journal = Fertility and Sterility|volume=98|issue=6|pages=1481–9.e10|date=December 2012 | pmid = 22975113 | doi = 10.1016/j.fertnstert.2012.08.016 | doi-access = free }}</ref> However, when all different types of [[time-lapse embryo imaging]] devices, with or without morphokinetic scoring systems, are compared against conventional embryo assessment for IVF, there is insufficient evidence of a difference in live-birth, pregnancy, stillbirth or miscarriage to choose between them.<ref>{{cite journal|vauthors=Armstrong S, Bhide P, Jordan V, Pacey A, Marjoribanks J, Farquhar C|title=Time-lapse systems for embryo incubation and assessment in assisted reproduction | journal = The Cochrane Database of Systematic Reviews|volume=5|issue=5|pages=CD011320|date=May 2019|pmid=31140578|pmc=6539473|doi = 10.1002/14651858.CD011320.pub4 }}</ref> Active efforts to develop a more accurate embryo selection analysis based on Artificial Intelligence and Deep Learning are underway. Embryo Ranking Intelligent Classification Assistant ([[Embryo Ranking Intelligent Classification Algorithm|ERICA]]),<ref>{{Cite web|url=https://embryoranking.com/ |title = ERICA Embryo Ranking &#124; Artificial Intelligence for Assisted Reproduction}}</ref> is a clear example.  This Deep Learning software substitutes manual classifications with a ranking system based on an individual embryo's predicted genetic status in a non-invasive fashion.<ref>{{Cite journal | doi=10.1016/j.fertnstert.2019.07.715| title=Artificial vision and machine learning designed to predict PGT-A results|journal=Fertility and Sterility| volume=112| issue=3| pages=e231| year=2019| vauthors = Chavez-Badiola A, Farias AF, Mendizabal-Ruiz G, Drakeley AJ, Garcia-Sánchez R, Zhang JJ | doi-access=free}}</ref> Studies on this area are still pending and current feasibility studies support its potential.<ref>{{cite journal|vauthors = Chavez-Badiola A, Flores-Saiffe Farias A, Mendizabal-Ruiz G, Garcia-Sanchez R, Drakeley AJ, Garcia-Sandoval JP|title=Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning|journal = Scientific Reports|volume = 10|issue=1|pages=4394|date=March 2020|pmid=32157183|pmc=7064494|doi = 10.1038/s41598-020-61357-9|bibcode = 2020NatSR..10.4394C}}</ref>