Approaches for prediction of the implantation potential of human embryos

Article info: Received: 9 November 2012 In revised form: 29 November 2012 Accepted: 30 November 2012 ABSTRACT Optimization of assisted reproductive technologies (ART) has become the main goal of contemporary reproductive medicine. The main aspiration of scientists working in the field is to use less intervention to achieve more, and, if possible, in a more cost-effective way. A number of directions have been under development, namely – various stimulation protocols, ART with no stimulation whatever, all aiming at a single goal – the chase for Moby Dick, or the perfect embryo. Comprehensive embryo selection resulting in reducing the number of transferred embryos is one of the main directions for optimization of the ART procedures. Both clinical and laboratory procedures are being constantly improved, and today there is a significant number of clinics that report success rates of 30% and even higher. Based on results achieved, and analyzing data from millions of ART procedures, researchers from different centers are seeking to develop prognostic models in order to further improve success rates. One of the greatest challenges remains the reduction of the incidence of multifetal pregnancy, and that can be achieved only through reducing the number of embryos per transfer and a rise in single embryo transfer (SET) numbers. This, however, depends on reliable methods for preliminary embryo selection, employing a growing number of morphological, biochemical, genetic and other characteristics of the embryo. A primary concern in developing prognostic models for in vitro fertilization (IVF) outcome is selecting the prognostic parameters to be included. A number of publications define the main criteria that have an impact on fertilization outcome on the side of the embryo, and for the ultimate outcome of the ART procedure – on the side of the maternal organism as a whole. In this review, some of the most important parameters are discussed, with particular focus on their application for development of IVF prognostic models.