Non-invasive selection for euploid embryos: prospects and pitfalls of the three most promising approaches

The objective of this review was to evaluate the efficacy of three promising technologies for assessment of ploidy status in IVF embryos [i.e. preimplantation genetic testing for aneuploidy (PGT-A)]: artificial intelligence (AI), non-invasive PGT-A (niPGT-A) and metabolomics. Publications where >80% correlation with blastocyst biopsies could be demonstrated in ≥50 cycles were prioritized. AI was found to classify the chance of an embryo implanting with an average area under the curve (AUC) of 0.7. AI is thus a superior selection method compared with morphological selection alone, but is still inferior to invasive PGT-A. Some niPGT-A studies have up to 100% concordance with PGT-A, but a multicentre study showed 78% concordance due to maternal contamination, which can improve with specific changes in culture conditions. niPGT-A has thus improved significantly and has the potential to reach 100% with PGT-A if the issue of maternal contamination is solved; however, >30% of euploid embryos never implant. Finally, metabolomics is the least developed technique of the three, but some preliminary data show >90% concordance with implantation and with PGT-A without changing culture conditions. Metabolomics thus has the potential to identify euploid embryos that, metabolically, are incapable of implanting. A combination of two or all of these approaches is possible.