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ART: Laboratory Aspects

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Clinical Reproductive Medicine and Surgery

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Abstract

Assisted reproductive technologies (ART) can be defined as fertility treatment that involves removing eggs from a woman’s ovaries and combining them with sperm in a laboratory. Methods used to achieve this result include in vitro fertilization (IVF), gamete intrafallopian transfer (GIFT), and zygote intrafallopian transfer (ZIFT). Currently, more than 300,000 cycles of human IVF and similar techniques are performed each year in the United States, resulting in the birth of over 80,000 babies. Far-reaching advances in laboratory techniques and culture conditions have been made since 1978, when the first IVF baby was born in England. Some of these advancements include in vitro oocyte maturation, intracytoplasmic sperm injection (ICSI), time-lapse imaging, preimplantation genetic testing (PGT), oocyte and embryo vitrification, and automated assessment and selection of embryos using artificial intelligence. Today, ART procedures are responsible for over 1% of all children born in the United States annually.

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References

  1. Edwards RG, Bavister BD, Steptoe PC. Early stages of fertilization in vitro of human oocytes matured in vitro. Nature. 1969;221(5181):632–5.

    Article  CAS  Google Scholar 

  2. Chang MC. Fertilization of rabbit ova in vitro. Nature. 1959;184(Suppl 7):466–7.

    Article  Google Scholar 

  3. Johnson MH. Robert Edwards: the path to IVF. Reprod Biomed Online. 2011;23(2):245–62.

    Article  Google Scholar 

  4. Centers for Disease Control and Prevention. 2017 assisted reproductive technology fertility clinic success rates report. Accessed 1 Oct 2020. https://www.cdc.gov/art/reports/2017/fertility-clinic.html.

  5. Gremeau AS, Andreadis N, Fatum M, Craig J, Turner K, McVeigh E, et al. In vitro maturation or in vitro fertilization for women with polycystic ovaries? A case–control study of 194 treatment cycles. Fertil Steril. 2012;98(2):355–60.

    Article  Google Scholar 

  6. Mikkelsen AL. Strategies in human in-vitro maturation and their clinical outcome. Reprod Biomed Online. 2005;10(5):593–9.

    Article  Google Scholar 

  7. Palermo G, Joris H, Devroey P, Van Steirteghem AC. Pregnancies after intracytoplasmic injection of single spermatozoon into an oocyte. Lancet. 1992;340(8810):17–8.

    Article  CAS  Google Scholar 

  8. Gardner DK, Weissman A, Howles CM, Shoham Z. Textbook of assisted reproductive technologies: laboratory and clinical perspectives. 3rd ed. London: Taylor & Francis; 2008.

    Book  Google Scholar 

  9. Balaban B, Yakin K, Urman B. Randomized comparison of two different blastocyst grading systems. Fertil Steril. 2006;85(3):559–63.

    Article  Google Scholar 

  10. Wong CC, Loewke KE, Bossert NL, Behr B, De Jonge CJ, Baer TM, Reijo Pera RA. Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat Biotechnol. 2010;28:1115–21.

    Article  CAS  Google Scholar 

  11. Conaghan J, Chen AA, Willman SP, Ivani K, Chenette PE, Boostanfar R, et al. Improving embryo selection using a computer-automated time-lapse image analysis test plus day 3 morphology: results from a prospective multicenter trial. Fertil Steril. 2013;100(2):412–9.e5. https://doi.org/10.1016/j.fertnstert.2013.04.021.

    Article  PubMed  Google Scholar 

  12. Kirkegaard K, Ahlström A, Ingerslev HJ, Hardarson T. Choosing the best embryo by time lapse versus standard morphology. Fertil Steril. 2015;103(2):323–32. https://doi.org/10.1016/j.fertnstert.2014.11.003.

    Article  PubMed  Google Scholar 

  13. Kaser DJ, Bormann CL, Missmer SA, Farland LV, Ginsburg ES, Racowsky C. Eeva™ pregnancy pilot study: a randomized controlled trial of single embryo transfer (SET) on day 3 or day 5 with or without time-lapse imaging (TLI) selection. Fertil Steril. 2016;106(3) https://doi.org/10.1016/j.fertnstert.2016.07.886.

  14. Chen M, Wei S, Hu J, Yuan J, Liu F. Does time-lapse imaging have favorable results for embryo incubation and selection compared with conventional methods in clinical in vitro fertilization? A meta-analysis and systematic review of randomized controlled trials. PLoS One. 2017;12(6):e0178720. https://doi.org/10.1371/journal.pone.0178720.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Goodman LR, Goldberg J, Falcone T, Austin C, Desai N. Does the addition of time-lapse morphokinetics in the selection of embryos for transfer improve pregnancy rates? A randomized controlled trial. Fertil Steril. 2016;105(2):275–85.

    Article  Google Scholar 

  16. Practice Committee of the American Society for Reproductive Medicine and Practice Committee of the Society for Assisted Reproductive Technology. Role of assisted hatching in in vitro fertilization: a guidline. Fertil Steril. 2014;102(2):348–51.

    Article  Google Scholar 

  17. Rall WF, Fahy GM. Ice-free cryopreservation of mouse embryos at −196 degrees C by vitrification. Nature. 1985;313(6003):573–5.

    Article  CAS  Google Scholar 

  18. Edgar DH, Gook DA. A critical appraisal of cryopreservation (slow cooling versus vitrification) of human oocytes and embryos. Hum Reprod Update. 2012;18(5):536–54.

    Article  Google Scholar 

  19. Bormann CL, Thirumalaraju P, Kanakasabapathy MK, Kandula H, Souter I, Dimitriadis I, Gupta R, Pooniwala R, Shafiee H. Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil Steril. 2020;113(4):781–7.

    Article  Google Scholar 

  20. Thirumalaraju P, Kanakasabapathy MK, Bormann CL, Kandula H, Pavan SKS, Yarravarapu D, Shafiee H. Human sperm morphology analysis using smartphone microscopy and deep learning. Fertil Steril. 2019;112(3, Suppl):e41.

    Article  Google Scholar 

  21. Targosz A, Przystałka P, Wiaderkiewicz R, Mrugacz G. Semantic segmentation of human oocyte images using deep neural networks. Biomed Eng Online. 2021;20(1):40. https://doi.org/10.1186/s12938-021-00864-w. PMID: 33892725; PMCID: PMC8066497

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kanakasabapathy M, Bormann C, Thirumalaraju P, Banerjee R, Shafiee H. Improving the performance of deep convolutional neural networks (CNN) in embryology using synthetic machine-generated images. In: Human reproduction, vol. 35. Oxford: Oxford Univ Press; 2020.

    Google Scholar 

  23. Dickinson J, Meyer A, Kelly N, Thirumalaraju P, Kanakasabapathy M, Kartik D, Bormann C, Shafiee H. Advancement in the future automation of ICSI: use of deep convolutional neural networks (CNN) to identify precise location to inject sperm in mature human oocytes. In: Human reproduction, vol. 35. Oxford: Oxford Univ Press; 2020.

    Google Scholar 

  24. Dimitriadis I, Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Souter I, Rice ST, Bhowmick P, Shafiee H. Deep convolutional neural networks (CNN) for assessment and selection of normally fertilized human embryos. Fertil Steril. 2019;112:e272.

    Article  Google Scholar 

  25. Bormann CL, Curchoe CL, Thirumalaraju P, Kanakasabapathy MK, Gupta R, Pooniwala R, Kandula H, Souter I, Dimitriadis I, Shafiee H. Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory. J Assist Reprod Genet. 2021; https://doi.org/10.1007/s10815-021-02198-x. Epub ahead of print.

  26. Kanakasabapathy MK, Thirumalaraju P, Bormann CL, Gupta R, Pooniwala R, Kandula H, Souter I, Dimitriadis I, Shafiee H. Deep learning mediated single time-point image-based prediction of embryo developmental outcome at the cleavage stage. arXiv preprint arXiv. 2020;2006.08346:5–21.

    Google Scholar 

  27. Kelly N, Bormann CL, Dickinson J, Meyer AD, Kanakasabapathy MK, Thirumalaraju P, Shafiee H. Future of automation: use of deep convolutional neural networks (CNN) to identify precise location to perform laser assisted hatching on human cleavage stage embryos. Fertil Steril. 2020;114(3, Suppl):e144.

    Article  Google Scholar 

  28. Thirumalaraju P, Kanakasabapathy MK, Bormann CL, Gupta R, Pooniwala R, Kandula H, Souter I, Dimitriadis I, Shafiee H. Evaluation of deep convolutional neural networks in classifying human embryo images based on their morphological quality. Heliyon. 2021;7(2):E06298.

    Article  Google Scholar 

  29. Bormann CL, Kanakasabapathy MK, Thirumalaraju P, Gupta R, Pooniwala R, Kandula H, Hariton E, Souter I, Dimitriadis I, Ramirez LB, Curchoe CL, Swain J, Boehnlein LM, Shafiee H. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020;9:E55301.

    Article  CAS  Google Scholar 

  30. Chavez-Badiola A, Flores-Saiffe Farias A, Mendizabal-Ruiz G, Garcia-Sanchez R, Drakeley AJ, Garcia-Sandoval JP. Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Sci Rep. 2020;10(1):4394. https://doi.org/10.1038/s41598-020-61357-9. PMID: 32157183; PMCID: PMC7064494

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Tran D, Cooke S, Illingworth PJ, Gardner DK. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum Reprod. 2019;34(6):1011–8. https://doi.org/10.1093/humrep/dez064. PMID: 31111884; PMCID: PMC6554189

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Meyer A, Dickinson J, Kelly N, Kandula H, Kanakasabapathy M, Thirumalaraju P, Bormann C, Shafiee H. Can deep convolutional neural network (CNN) be used as a non-invasive method to replace Preimplantation Genetic Testing for Aneuploidy (PGT-A)? In: Human reproduction, vol. 35. Oxford: Oxford Univ Press; 2020. p. I238–8.

    Google Scholar 

  33. Chavez-Badiola A, Flores-Saiffe-Farías A, Mendizabal-Ruiz G, Drakeley AJ, Cohen J. Embryo Ranking Intelligent Classification Algorithm (ERICA): artificial intelligence clinical assistant predicting embryo ploidy and implantation. Reprod Biomed Online. 2020;41(4):585–93. https://doi.org/10.1016/j.rbmo.2020.07.003. Epub 2020 Jul 5

    Article  CAS  PubMed  Google Scholar 

  34. Chavez-Badiola A, Zhang J, Flores-Saiffe-Farías A, Mendizabal-Ruiz G, Drakeley AJ, Olcha M. Non-invasive chromosome screening and its correlation against ranking prediction made by erica, a deep-learning embryo ranking algorithm. Fertil Steril. 2020;114(3):e436–7.

    Article  Google Scholar 

  35. Kanakasabapathy MK, Hammer KC, Dickinson K, Veiga C, Kelly F, Thirumalaraju P, Bormann CL, Shafiee H. Using artificial intelligence to avoid human error in identifiying embryos. Fertil Steril. 2020;113(4):e45.

    Article  Google Scholar 

  36. Bormann CL, Curchoe CL, Thirumalaraju P, Kanakasabapathy MK, Gupta R, Pooniwala R, Kandula H, Souter I, Dimitriadis I, Shafiee H. Deep learning early warning system for embryo culture conditions and embryologist performance in the ART laboratory. J Assist Reprod Genet. 2021; https://doi.org/10.1007/s10815-021-02198-x.

  37. Fischer B, Bavister BD. Oxygen tension in the oviduct and uterus of rhesus monkeys, hamsters and rabbits. J Reprod Fertil. 1993;99(2):673–9.

    Article  CAS  Google Scholar 

  38. Karagenc L, Sertkaya Z, Ciray N, Ulug U, Bahceci M. Impact of oxygen concentration on embryonic development of mouse zygotes. Reprod Biomed Online. 2004;9(4):409–17.

    Article  CAS  Google Scholar 

  39. Bontekoe S, Mantikou E, van Wely M, Seshadri S, Repping S, Mastenbroek S. Low oxygen concentrations for embryo culture in assisted reproductive technologies. Cochrane Database Syst Rev. 2012;(7):CD008950.

    Google Scholar 

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Acknowledgments

The author would like to acknowledge the contributions of Drs. Beth Plante, Gary D. Smith, and Sandra Ann Carson who were the authors of this chapter in a previous edition.

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Authors and Affiliations

  1. Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Charles L. Bormann

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  1. Charles L. Bormann
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Correspondence to Charles L. Bormann .

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Editors and Affiliations

  1. Cleveland Clinic London, Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA

    Tommaso Falcone

  2. Division of Reproductive Endocrinology and Infertility, University of Alabama School of Medicine, Birmingham, AL, USA

    William W. Hurd

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Bormann, C.L. (2022). ART: Laboratory Aspects. In: Falcone, T., Hurd, W.W. (eds) Clinical Reproductive Medicine and Surgery. Springer, Cham. https://doi.org/10.1007/978-3-030-99596-6_18

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  • DOI: https://doi.org/10.1007/978-3-030-99596-6_18

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