Literature DB >> 34593324

Focus on time-lapse analysis: blastocyst collapse and morphometric assessment as new features of embryo viability.

Romualdo Sciorio1, Marcos Meseguer2.   

Abstract

The main goal of assisted reproductive technology (ART) is to achieve a healthy singleton live birth after the transfer of one embryo. A major objective of IVF scientists has always been to use adequate criteria for selecting the embryo for transfer according to its implantation potential. Indeed, embryo quality is usually assessed by evaluating visual morphology, which relies on the removal of the embryo from the incubator and might include inter- and intra-evaluator variation among embryologists. Recently, an advancement in embryo culture has taken place with the introduction of a new type of incubator with an integrated time-lapse monitoring system, which enables embryologists to analyse the dynamic events of embryo development from fertilization to blastocyst formation. This novel practice is rapidly growing and has been used in many IVF centres worldwide. Therefore, the main aim of this review is to present the benefits of time-lapse monitoring in a modern embryology laboratory; in particular, we discuss blastocyst collapse and morphometric blastocyst assessment, and analyse their association with embryo viability and implantation potential. In addition, we highlight preliminary studies involving artificial intelligence and machine learning models as non-invasive markers of clinical pregnancy.
Copyright © 2021 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Blastocyst collapse; Embryo viability; Morphometric blastocyst assessment; Pregnancy outcomes; Time-lapse monitoring; Undisturbed embryo culture

Mesh:

Year:  2021        PMID: 34593324     DOI: 10.1016/j.rbmo.2021.08.008

Source DB:  PubMed          Journal:  Reprod Biomed Online        ISSN: 1472-6483            Impact factor:   3.828


  2 in total

1.  Detecting Blastocyst Components by Artificial Intelligence for Human Embryological Analysis to Improve Success Rate of In Vitro Fertilization.

Authors:  Muhammad Arsalan; Adnan Haider; Jiho Choi; Kang Ryoung Park
Journal:  J Pers Med       Date:  2022-01-18

2.  Human Blastocyst Components Detection Using Multiscale Aggregation Semantic Segmentation Network for Embryonic Analysis.

Authors:  Muhammad Arsalan; Adnan Haider; Se Woon Cho; Yu Hwan Kim; Kang Ryoung Park
Journal:  Biomedicines       Date:  2022-07-15
  2 in total

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