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Chapter 34 - Embryo Culture by Time-Lapse: Selection and Beyond

from Section 6 - Embryo Assessment: Morphology and Beyond

Published online by Cambridge University Press:  07 August 2023

Markus H. M. Montag
Affiliation:
ilabcomm GmbH, St Augustin, Germany
Dean E. Morbeck
Affiliation:
Kindbody Inc, New York City
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Summary

One of the most innovative changes to the practice of human embryo culture was the introduction of sophisticated time-lapse imaging (TLI) systems that eventually became part of the incubation unit. TLI allows continuous, uninterrupted monitoring of embryo development. Embryo selection at either the cleavage or the blastocyst stage using algorithms developed with tens of thousands or more of embryos with known implantation is robust and repeatable. The technology has continued to evolve, with improvements to the physical technology as well as software enhancements, including artificial intelligence (AI)-based embryo selection algorithms and machine learning.

Type
Chapter
Information
Principles of IVF Laboratory Practice
Laboratory Set-Up, Training and Daily Operation
, pp. 251 - 254
Publisher: Cambridge University Press
Print publication year: 2023

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References

Pribenszky, C., Matyas, S., Kovacs, P., et al. Pregnancy achieved by transfer of a single blastocyst selected by time-lapse monitoring. Reprod Biomed Online 2010; 21:533–6.CrossRefGoogle ScholarPubMed
Cruz, M., Gadea, B., Garrido, N., et al. Embryo quality, blastocyst and ongoing pregnancy rates in oocyte donation patients whose embryos were monitored by time-lapse imaging. J Assist Reprod Genet 2011; 28:569–73.CrossRefGoogle ScholarPubMed
Kragh, M. F., Rimestad, J., Berntsen, J. and Karstoft, H. Automatic grading of human blastocysts from time-lapse imaging. Comput Biol Med 2019; 115:103494.CrossRefGoogle ScholarPubMed
Hammond, E. R., Foong, A. K. M., Rosli, N. and Morbeck, D.E.. Should we freeze it? Agreement on fate of borderline blastocysts is poor and does not improve with a modified blastocyst grading system. Hum Reprod 2020; 35:1045–53.Google Scholar
ESHRE SIG Embryology, Alpha Scientists in Reproductive Medicine. The Vienna consensus: report of an expert meeting on the development of ART laboratory performance indicators. Reprod Biomed Online 2017; 35:494510.CrossRefGoogle Scholar
Munch, E. M., Sparks, A. E., Duran, H. E. and Van Voorhis, B. J. Lack of carbon air filtration impacts early embryo development. J Assist Reprod Genet 2015; 32:1009–17.CrossRefGoogle ScholarPubMed
Morbeck, D. E. Time-lapse implementation in a clinical setting: management of laboratory quality, in Time-Lapse Microscopy in In-Vitro Fertilization, ed. Meseguer, M., pp. 128–30 (Cambridge: Cambridge University Press, 2016).Google Scholar
Turner, T. The identification of a toxic substance in the in vitro fertilization laboratory: the value of inter-laboratory communication. Fertil Mag 2010; 12:64–5.Google Scholar
Hammond, E. R. and Morbeck, D. E. Tracking quality: can embryology key performance indicators be used to identify clinically relevant shifts in pregnancy rate? Hum Reprod 2019; 34:3743.Google Scholar
Wolff, H. S., Fredrickson, J. R., Walker, D. L. and Morbeck, D. E. Advances in quality control: mouse embryo morphokinetics are sensitive markers of in vitro stress. Hum Reprod 2013; 28:1776–82.CrossRefGoogle ScholarPubMed
Bormann, C. L., Curchoe, C. L., Thirumalaraju, P., et al. Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory. J Assist Reprod Genet 2021; 38:1641–6.Google ScholarPubMed
Mortimer, S. and Mortimer, D. How are we doing?: Benchmarking, in Quality and Risk Management in the IVF Laboratory, pp. 145–52 (Cambridge: Cambridge University Press, 2015).CrossRefGoogle Scholar

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