Literature DB >> 27090968

Conventional morphology performs better than morphokinetics for prediction of live birth after day 2 transfer.

Aisling Ahlstrom1, Hannah Park2, Christina Bergh2, Ulrika Selleskog2, Kersti Lundin2.   

Abstract

Numerous studies have reported on the potential value of time-lapse variables for prediction of embryo viability. However, these variables have not been evaluated in combination with conventional morphological grading and patient characteristics. The aim of this study was to assess the ability of patient characteristics and embryo morphology together with morphokinetic variables to predict live birth after day 2 transfer. This retrospective analysis included 207 transferred embryos from 199 couples cultured in a time-lapse system up to day 2 of development. Good prediction of live birth or ranking of embryos with respect to live birth potential was achieved with early cleavage combined with fragmentation grade at 43-45 h. These variables were selected as the strongest predictors of live birth, as assessed by stepwise logistic regression, and additional inclusion of morphokinetic variables did not improve the model significantly. Also, neither logistic regression models nor classification tree models with morphokinetic variables were able to achieve equally good prediction of live birth, as measured by AUC on an external data set not used for model development. In conclusion, for fresh day 2 transfers early cleavage in combination with fragmentation grade at 43-45 h should be considered when selecting between good quality embryos.
Copyright © 2016 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  IVF; early cleavage; embryo selection; live birth; time-lapse

Mesh:

Year:  2016        PMID: 27090968     DOI: 10.1016/j.rbmo.2016.03.008

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


  8 in total

1.  Inter-laboratory agreement on embryo classification and clinical decision: Conventional morphological assessment vs. time lapse.

Authors:  Luis Martínez-Granados; María Serrano; Antonio González-Utor; Nereyda Ortíz; Vicente Badajoz; Enrique Olaya; Nicolás Prados; Montse Boada; Jose A Castilla
Journal:  PLoS One       Date:  2017-08-25       Impact factor: 3.240

2.  Prediction of blastocyst development and implantation potential in utero based on the third cleavage and compaction times in mouse pre-implantation embryos.

Authors:  Jihyun Kim; Seok Hyun Kim; Jin Hyun Jun
Journal:  J Reprod Dev       Date:  2016-12-16       Impact factor: 2.214

3.  Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer.

Authors:  D Tran; S Cooke; P J Illingworth; D K Gardner
Journal:  Hum Reprod       Date:  2019-06-04       Impact factor: 6.918

4.  Can novel early non-invasive biomarkers of embryo quality be identified with time-lapse imaging to predict live birth?

Authors:  J Barberet; C Bruno; E Valot; C Antunes-Nunes; L Jonval; J Chammas; C Choux; P Ginod; P Sagot; A Soudry-Faure; P Fauque
Journal:  Hum Reprod       Date:  2019-08-01       Impact factor: 6.918

5.  Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.

Authors:  Bo Huang; Shunyuan Zheng; Bingxin Ma; Yongle Yang; Shengping Zhang; Lei Jin
Journal:  BMC Pregnancy Childbirth       Date:  2022-01-16       Impact factor: 3.007

6.  A double-blind randomized controlled trial investigating a time-lapse algorithm for selecting Day 5 blastocysts for transfer.

Authors:  Aisling Ahlström; Kersti Lundin; Anna-Karin Lind; Kristina Gunnarsson; Göran Westlander; Hannah Park; Anna Thurin-Kjellberg; Steinunn A Thorsteinsdottir; Snorri Einarsson; Mari Åström; Kristina Löfdahl; Judith Menezes; Susanne Callender; Cina Nyberg; Jens Winerdal; Camilla Stenfelt; Brit-Randi Jonassen; Nan Oldereid; Lisa Nolte; Malin Sundler; Thorir Hardarson
Journal:  Hum Reprod       Date:  2022-04-01       Impact factor: 6.353

Review 7.  Time-lapse technology for embryo culture and selection.

Authors:  Kersti Lundin; Hannah Park
Journal:  Ups J Med Sci       Date:  2020-02-25       Impact factor: 2.384

Review 8.  Reporting on the Value of Artificial Intelligence in Predicting the Optimal Embryo for Transfer: A Systematic Review including Data Synthesis.

Authors:  Konstantinos Sfakianoudis; Evangelos Maziotis; Sokratis Grigoriadis; Agni Pantou; Georgia Kokkini; Anna Trypidi; Polina Giannelou; Athanasios Zikopoulos; Irene Angeli; Terpsithea Vaxevanoglou; Konstantinos Pantos; Mara Simopoulou
Journal:  Biomedicines       Date:  2022-03-17
  8 in total

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