Laboratories have developed grading methods to judge ovocyte and embryo quality. In order to optimise pregnancy rates, there is significant evidence that a morphological scoring system is the best strategy for the selection of embryos. 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 rates further. 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. Active efforts to develop a more accurate embryo selection analysis based on Artificial Intelligence and Deep Learning are underway. Embryo Ranking Intelligent Classification Assistant (ERICA), 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. Studies on this area are still pending and current feasibility studies support its potential.