AI Article Synopsis

  • In-vitro fertilization (IVF) helps couples who have trouble getting pregnant by creating embryos outside the body and transferring the best one to increase the chance of a healthy baby.
  • New technologies like time-lapse monitoring allow scientists to watch embryos grow continuously, which gives better information about their development than traditional methods.
  • Researchers are now looking into new non-invasive ways to check embryos using things like waste from the IVF process and artificial intelligence, while also discussing the pros and cons of current methods and what challenges lie ahead.

Article Abstract

In-vitro fertilization (IVF) aims at overcoming the causes of infertility and lead to a healthy live birth. To maximize IVF efficiency, it is critical to identify and transfer the most competent embryo within a cohort produced by a couple during a cycle. Conventional static embryo morphological assessment involves sequential observations under a light microscope at specific timepoints. The introduction of time-lapse technology enhanced morphological evaluation via the continuous monitoring of embryo preimplantation in vitro development, thereby unveiling features otherwise undetectable via multiple static assessments. Although an association exists, blastocyst morphology poorly predicts chromosomal competence. In fact, the only reliable approach currently available to diagnose the embryonic karyotype is trophectoderm biopsy and comprehensive chromosome testing to assess non-mosaic aneuploidies, namely preimplantation genetic testing for aneuploidies (PGT-A). Lately, the focus is shifting towards the fine-tuning of non-invasive technologies, such as "omic" analyses of waste products of IVF (e.g., spent culture media) and/or artificial intelligence-powered morphologic/morphodynamic evaluations. This review summarizes the main tools currently available to assess (or predict) embryo developmental, chromosomal, and reproductive competence, their strengths, the limitations, and the most probable future challenges.

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Source
http://dx.doi.org/10.23736/S2724-606X.23.05296-XDOI Listing

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