Danilo Cimadomo, Federica Innocenti, Marilena Taggi, Gaia Saturno, Maria R Campitiello, Maurizio Guido, Alberto Vaiarelli, Filippo M Ubaldi, Laura Rienzi
{"title":"How should the best human embryo in vitro be? Current and future challenges for embryo selection.","authors":"Danilo Cimadomo, Federica Innocenti, Marilena Taggi, Gaia Saturno, Maria R Campitiello, Maurizio Guido, Alberto Vaiarelli, Filippo M Ubaldi, Laura Rienzi","doi":"10.23736/S2724-606X.23.05296-X","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":18572,"journal":{"name":"Minerva obstetrics and gynecology","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Minerva obstetrics and gynecology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23736/S2724-606X.23.05296-X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/6/16 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"OBSTETRICS & GYNECOLOGY","Score":null,"Total":0}
引用次数: 0
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.