Skip to main content
Log in

Time-lapse-Monitoring – Pro und Kontra

Time-lapse monitoring: pros and cons

  • In der Diskussion
  • Published:
Gynäkologische Endokrinologie Aims and scope

Zusammenfassung

Die medizinisch unterstützte Fortpflanzung („medically assisted reproduction“ [MAR]) hat sich seit den ersten erfolgreichen Schritten vor über 40 Jahren kontinuierlich weiterentwickelt. So sind die Kulturmedien und Kulturumgebungen viel komplexer als zu Beginn und können die Präimplantationsentwicklung der Embryonen bis zum Transfer – normalerweise am fünften Tag – optimal unterstützen. Allerdings ist die größte Herausforderung für das In-vitro-Fertilisations(IVF)-Labor die Identifizierung des einen Embryos mit dem besten Implantationspotenzial. Die Selektion erfolgt klassischerweise anhand bestimmter morphologischer Veränderungen in einer zeitlich korrekten Reihenfolge. Neue Techniken, wie das Time-lapse(TL)-Monitoring, haben interessante und vor allem dynamische Ereignisse in deren zeitlicher Entwicklung (Morphokinetik) für uns erstmals sichtbar gemacht. Viele dieser neuen morphokinetischen Parameter korrelieren mit dem Implantationspotenzial der einzelnen Blastozyste. Allerdings zeigen neueste Studien, dass der klinische Nutzen möglicherweise nicht in dem Maße vorhanden ist, wie das TL-Monitoring es verspricht. In der vorliegenden Zusammenfassung soll die aktuelle Studienlage kurz beleuchtet werden, zudem werden Vor- und Nachteile aufgeführt.

Abstract

Medically assisted reproduction (MAR) has continuously developed since the first successful steps over 40 years ago. For example, culture media and culture environment are far more complex than they were in the beginning and can optimally support the pre-implantation development of the embryos until transfer. However, the biggest challenge for the in vitro fertilization (IVF) laboratory is to identify the one embryo with the best implantation potential. Classically, selection is based on certain morphological changes in a distinct chronological order. However, new technologies, such as time-lapse (TL) monitoring, have made interesting and highly dynamic events in a temporal manner (morphokinetics) visible to us for the first time. Many of these new morphokinetic parameters correlate with the implantation potential of an individual blastocyst. However, recent studies show that the clinical benefit may not be as high as promised. This summary will briefly review the current state of research and list the advantages and disadvantages.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Literatur

  1. De Vos A, Van Landuyt L, Santos-Ribeiro S, Camus M, Van de Velde H, Tournaye H, Verheyen G (2016) Cumulative live birth rates after fresh and vitrified cleavage-stage versus blastocyst-stage embryo transfer in the first treatment cycle. Hum Reprod 31:2442–2449

    PubMed  Google Scholar 

  2. Sullivan EA, Wang YA, Hayward I, Chambers GM, Illingworth P, McBain J, Norman RJ (2012) Single embryo transfer reduces the risk of perinatal mortality, a population study. Hum Reprod 27:3609–3615

    PubMed  Google Scholar 

  3. Marconi N, Allen CP, Bhattacharya S, Maheshwari A (2022) Obstetric and perinatal outcomes of singleton pregnancies after blastocyst-stage embryo transfer compared with those after cleavage-stage embryo transfer: a systematic review and cumulative meta-analysis. Hum Reprod Update 28(2):255–281

    PubMed  Google Scholar 

  4. Raja EA, Bhattacharya S, Maheshwari A, McLernon DJ (2023) A comparison of perinatal outcomes following fresh blastocyst or cleavage stage embryo transfer in singletons and twins and between singleton siblings. Hum Reprod Open. https://doi.org/10.1093/hropen/hoad003

    Article  PubMed  PubMed Central  Google Scholar 

  5. Alpha Scientists in Reproductive Medicine and ESHRE Special Interest Group of Embryology (2011) The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting. Hum Reprod 26(6):1270–1283

    Google Scholar 

  6. De los SMJ, Apter S, Coticchio G, Debrock S, Lundin K, Plancha CE, Prados F, Rienzi L, Verheyen G, Woodward B et al (2016) Revised guidelines for good practice in IVF laboratories (2015). Hum Reprod 31:685–686

    Google Scholar 

  7. Storr A, Venetis CA, Cooke S, Kilani S, Ledger W (2017) Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study. Hum Reprod 32(2):307–314

    PubMed  Google Scholar 

  8. Cimadomo D, Sosa Fernandez L, Soscia D, Fabozzi G, Benini F, Cesana A, Dal Canto MB, Maggiulli R, Muzzì S, Scarica C, Rienzi L, De Santis L (2022) Inter-centre reliability in embryo grading across several IVF clinics is limited: implications for embryo selection. Reprod Biomed Online 44(1):39–48

    PubMed  Google Scholar 

  9. Fordham DE, Rosentraub D, Polsky AL, Aviram T, Wolf Y, Perl O, Devir A, Rosentraub S, Silver DH, Gold ZY, Bronstein AM, Lara LM, Nagi BJ, Alvarez A, Munné S (2022) Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity? Hum Reprod 37(10):2275–2290

    PubMed  Google Scholar 

  10. Meseguer M, Herrero J, Tejera A, Hilligsoe KM, Ramsing NB, Remohi J (2011) The use of morphokinetics as a predictor of embryo implantation. Hum Reprod 26:2658–2671

    PubMed  Google Scholar 

  11. Meseguer M, Rubio I, Cruz M, Basile N, Marcos J, Requena A (2012) Embryo incubation and selection in a time-lapse monitoring system improves pregnancy outcome compared with a standard incubator: a retrospective cohort study. Fertil Steril 98:1481–1489

    PubMed  Google Scholar 

  12. Zhang JQ, Li XL, Peng Y, Guo X, Heng BC, Tong GQ (2010) Reduction in exposure of human embryos outside the incubator enhances embryo quality and blastulation rate. Reprod Biomed Online 20:510–515

    PubMed  Google Scholar 

  13. Campbell A, Fishel S, Bowman N, Duffy S, Sedler M, Hickman CFL (2013) Modelling a risk classification of aneuploidy in human embryos using non-invasive morphokinetics. Reprod Biomed Online 26:477–485

    PubMed  Google Scholar 

  14. Coticchio G, Borini A, Zacà C, Makrakis E, Sfontouris I (2022) Fertilization signatures as biomarkers of embryo quality. Hum Reprod 37(8):1704–1711

    PubMed  Google Scholar 

  15. Athayde Wirka K, Chen AA, Conaghan J, Ivani K, Gvakharia M, Behr B, Suraj V, Tan L, Shen S (2014) Atypical embryo phenotypes identified by time-lapse microscopy: high prevalence and association with embryo development. Fertil Steril 101(e1635):1637–1648.e1‑5

    PubMed  Google Scholar 

  16. Chen L, Zhang S, Gu Y, Peng Y, Huang Z, Gong F, Lin G (2022) Vacuolization in embryos on days 3 and 4 of in vitro development: Association with stimulation protocols, embryo development, chromosomal status, pregnancy and neonatal outcomes. Front Endocrinol 13:985741

    Google Scholar 

  17. Lagalla C, Tarozzi N, Sciajno R, Wells D, Di Santo M, Nadalini M, Distratis V, Borini A (2017) Embryos with morphokinetic abnormalities may develop into euploid blastocysts. Reprod Biomed Online 34(2):137–146

    CAS  PubMed  Google Scholar 

  18. Coticchio G, Lagalla C, Sturmey R, Pennetta F, Borini A (2019) The enigmatic morula: mechanisms of development, cell fate determination, self-correction and implications for ART. Hum Reprod Update 25(4):422–438

    CAS  PubMed  Google Scholar 

  19. Hur C, Nanavaty V, Yao M, Desai N (2023) The presence of partial compaction patterns is associated with lower rates of blastocyst formation, sub-optimal morphokinetic parameters and poorer morphologic grade. Reprod Biol Endocrinol 21(1):12

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Tvrdonova K, Belaskova S, Rumpikova T, Malenovska A, Rumpik D, Myslivcova Fucikova A, Malir F (2021) Differences in morphokinetic parameters and incidence of multinucleations in human embryos of genetically normal, abnormal and euploid embryos leading to clinical pregnancy. J Clin Med 10(21):5173

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Sayed S, Reigstad MM, Petersen BM, Schwennicke A, Hausken JW, Storeng R (2022) Nucleation status of Day 2 pre-implantation embryos, acquired by time-lapse imaging during IVF, is associated with live birth. PLoS ONE 17(9):e274502

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Soukhov E, Karavani G, Szaingurten-Solodkin I, Alfayumi-Zeadna S, Elharar G, Richter D, Wainstock T, Zeadna A, Levitas E, Har-Vardi I (2022) Prediction of embryo implantation rate using a sole parameter of timing of starting blastulation. Zygote 30(4):501–508

    CAS  PubMed  Google Scholar 

  23. Eastick J, Venetis C, Cooke S, Chapman M (2023) Detailed analysis of cytoplasmic strings in human blastocysts: new insights. Zygote 31(1):78–84

    PubMed  Google Scholar 

  24. Sciorio R, Meseguer M (2021) Focus on time-lapse analysis: blastocyst collapse and morphometric assessment as new features of embryo viability. Reprod Biomed Online 43(5):821–832

    PubMed  Google Scholar 

  25. Cimadomo D, Marconetto A, Trio S, Chiappetta V, Innocenti F, Albricci L, Erlich I, Ben-Meir A, Har-Vardi I, Kantor B, Sakov A, Coticchio G, Borini A, Ubaldi FM, Rienzi L (2022) Human blastocyst spontaneous collapse is associated with worse morphological quality and higher degeneration and aneuploidy rates: a comprehensive analysis standardized through artificial intelligence. Hum Reprod 37(10):2291–2306

    PubMed  Google Scholar 

  26. Setti AS, Braga DPAF, Vingris L, Iaconelli A, Borges E (2022) Improved embryonic development and utilization rates with EmbryoScope: a within-subject comparison versus a benchtop incubator. Zygote 30(5):633–637

    PubMed  Google Scholar 

  27. Kermack AJ, Fesenko I, Christensen DR, Parry KL, Lowen P, Wellstead SJ, Harris SF, Calder PC, Macklon NS, Houghton FD (2022) Incubator type affects human blastocyst formation and embryo metabolism: a randomized controlled trial. Hum Reprod 37(12):2757–2767

    PubMed  Google Scholar 

  28. Armstrong S, Bhide P, Jordan V, Pacey A, Marjoribanks J, Farquhar C (2019) Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD011320.pub4

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ahlström A, Lundin K, Lind AK, Gunnarsson K, Westlander G, Park H, Thurin-Kjellberg A, Thorsteinsdottir SA, Einarsson S, Åström M, Löfdahl K, Menezes J, Callender S, Nyberg C, Winerdal J, Stenfelt C, Jonassen BR, Oldereid N, Nolte L, Sundler M, Hardarson T (2017) A double-blind randomized controlled trial investigating a time-lapse algorithm for selecting Day 5 blastocysts for transfer. Hum Reprod 37(4):708–717

    Google Scholar 

  30. Kieslinger DC, Vergouw CG, Ramos L, Arends B, Curfs MHJM, Slappendel E, Kostelijk EH, Pieters MHEC, Consten D, Verhoeven MO, Besselink DE, Broekmans F, Cohlen BJ, Smeenk JMJ, Mastenbroek S, de Koning CH, van Kasteren YM, Moll E, van Disseldorp J, Brinkhuis EA, Kuijper EAM, van Baal WM, van Weering HGI, van der Linden PJQ, Gerards MH, Bossuyt PM, van Wely M, Lambalk CB (2023) Clinical outcomes of uninterrupted embryo culture with or without time-lapse-based embryo selection versus interrupted standard culture (SelecTIMO): a three-armed, multicentre, double-blind, randomised controlled trial. Lancet 401(10386):1438–1446

    CAS  PubMed  Google Scholar 

  31. Conaghan J, Chen AA, Willman SP, Ivani K, Chenette PE, Boostanfar R, Baker VL, Adamson GD, Abusief ME, Gvakharia M, Loewke KE, Shen S (2013) Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial. Fertil Steril 100(2):412–419.e5

    PubMed  Google Scholar 

  32. Diamond MP, Suraj V, Behnke EJ, Yang X, Angle MJ, Lambe-Steinmiller JC, Watterson R, Athayde Wirka K, Chen AA, Shen S (2015) Using the Eeva Test™ adjunctively to traditional day 3 morphology is informative for consistent embryo assessment within a panel of embryologists with diverse experience. J Assist Reprod Genet 32(1):61–68

    PubMed  Google Scholar 

  33. Zhang XD, Zhang Q, Han W, Liu WW, Shen XL, Yao GD, Shi SL, Hu LL, Wang SS, Wang JX, Zhou JJ, Kang WW, Zhang HD, Luo C, Yu Q, Liu RZ, Sun YP, Sun HX, Wang XH, Quan S, Huang GN (2022) Comparison of embryo implantation potential between time-lapse incubators and standard incubators: a randomized controlled study. Reprod Biomed Online 45(5):858–866

    PubMed  Google Scholar 

  34. Riegler MA, Stensen MH, Witczak O, Andersen JM, Hicks SA, Hammer HL, Delbarre E, Halvorsen P, Yazidi A, Holst N, Haugen TB (2021) Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum Reprod 36(9):2429–2442

    CAS  PubMed  Google Scholar 

  35. Bormann CL, Thirumalaraju P, Kanakasabapathy MK, Kandula H, Souter I, Dimitriadis I, Gupta R, Pooniwala R, Shafiee H (2020) Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril 113(4):781–787

    PubMed  PubMed Central  Google Scholar 

  36. Curchoe CL, Bormann CL (2018) Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet. https://doi.org/10.1007/s10815-019-01408-x

    Article  Google Scholar 

  37. Liao Q, Zhang Q, Feng X, Huang H, Xu H, Tian B, Liu J, Yu Q, Guo N, Liu Q, Huang B, Ma D, Ai J, Xu S, Li K (2021) Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring. Commun Biol 4(1):415

    PubMed  PubMed Central  Google Scholar 

  38. Huang B, Zheng S, Ma B, Yang Y, Zhang S, Jin L (2022) Using deep learning to predict the outcome of live birth from more than 10,000 embryo data. BMC Pregnancy Childbirth 22(1):36

    PubMed  PubMed Central  Google Scholar 

  39. Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF (2022) Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS ONE 17(2):e262661

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Cimadomo D, Chiappetta V, Innocenti F, Saturno G, Taggi M, Marconetto A, Casciani V, Albricci L, Maggiulli R, Coticchio G, Ahlström A, Berntsen J, Larman M, Borini A, Vaiarelli A, Ubaldi FM, Rienzi L (2023) Towards automation in IVF: pre-clinical validation of a deep learning-based embryo grading system during PGT‑A cycles. J Clin Med 12(5):1806

    PubMed  PubMed Central  Google Scholar 

  41. Basile N, Nogales Mdel C, Bronet F, Florensa M, Riqueiros M, Rodrigo L, García-Velasco J, Meseguer M (2014) Increasing the probability of selecting chromosomally normal embryos by time-lapse morphokinetics analysis. Fertil Steril 101(3):699–704

    PubMed  Google Scholar 

  42. Minasi MG, Colasante A, Riccio T, Ruberti A, Casciani V, Scarselli F, Spinella F, Fiorentino F, Varricchio MT, Greco E (2016) Correlation between aneuploidy, standard morphology evaluation and morphokinetic development in 1730 biopsied blastocysts: a consecutive case series study. Hum Reprod 31(10):2245–2254

    PubMed  Google Scholar 

  43. Barnes J, Brendel M, Gao VR, Rajendran S, Kim J, Li Q, Malmsten JE, Sierra JT, Zisimopoulos P, Sigaras A, Khosravi P, Meseguer M, Zhan Q, Rosenwaks Z, Elemento O, Zaninovic N, Hajirasouliha I (2023) A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digit Health 5(1):e28–e40

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Berntsen J, Rimestad J, Lassen JT, Tran D, Kragh MF (2022) Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS ONE 17(2):e262661

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Verena Nordhoff.

Ethics declarations

Interessenkonflikt

V. Nordhoff, C. Sibold und J. Hirchenhain geben an, dass kein Interessenkonflikt besteht.

Additional information

Redaktion

Heribert Kentenich, Berlin

Wolfgang Küpker, Baden-Baden

Sibil Tschudin, Basel

Ludwig Wildt, Innsbruck

figure qr

QR-Code scannen & Beitrag online lesen

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nordhoff, V., Sibold, C. & Hirchenhain, J. Time-lapse-Monitoring – Pro und Kontra. Gynäkologische Endokrinologie 21, 211–216 (2023). https://doi.org/10.1007/s10304-023-00514-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10304-023-00514-5

Schlüsselwörter

Keywords

Navigation