The embryo non-invasive pre-implantation diagnosis era: how far are we?
Maite Del ColladoGabriella Mamede AndradeNatalia Juliana Nardelli GonçalvesSamuel FortiniFelipe PerecinMateus Maldonado CarrieroPublished in: Animal reproduction (2023)
Advancements in assisted reproduction (AR) methodologies have allowed significant improvements in live birth rates of women who otherwise would not be able to conceive. One of the tools that allowed this improvement is the possibility of embryo selection based on genetic status, performed via preimplantation genetic testing (PGT). Even though the widespread use of PGT from TE biopsy helped to decrease the interval from the beginning of the AR intervention to pregnancy, especially in older patients, in AR, there are still many concerns about the application of this invasive methodology in all cycles. Therefore, recently, researchers started to study the use of cell free DNA (cfDNA) released by the blastocyst in its culture medium to perform PGT, in a method called non-invasive PGT (niPGT). The development of a niPGT would bring the diagnostics power of conventional PGT, but with the advantage of being potentially less harmful to the embryo. Its implementation in clinical practice, however, is under heavy discussion since there are many unknowns about the technique, such as the origin of the cfDNA or if this genetic material is a true representative of the actual ploidy status of the embryo. Available data indicates that there is high correspondence between results observed in TE biopsies and the ones observed from cfDNA, but these results are still contradictory and highly debatable. In the present review, the advantages and disadvantages of niPGT are presented and discussed in relation to tradition TE biopsy-based PGT. Furthermore, there are also presented some other possible non-invasive tools that could be applied in the selection of the best embryo, such as quantification of other molecules as quality biomarkers, or the use artificial intelligence (AI) to identify the best embryos based on morphological and/or morphokitetic parameters.
Keyphrases
- pregnancy outcomes
- artificial intelligence
- big data
- pregnant women
- machine learning
- clinical practice
- ultrasound guided
- deep learning
- randomized controlled trial
- genome wide
- primary care
- type diabetes
- quality improvement
- polycystic ovary syndrome
- cross sectional
- preterm birth
- dna methylation
- electronic health record