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Automation, Artificial Intelligence and Innovations in the Future of IVF

  • Alex C. Varghese
  • Charalampos S. Siristatidis
Chapter

Abstract

By in vitro fertilization (IVF), more than 5 million babies have been born worldwide till now and the number is increasing. Many developments took place – among others – in the embryology laboratory in the last few decades. IVF lab deals with the precious human life – the gametes and embryos which are destined to become 100–200 trillion cells adult human being. The culture systems currently being used in IVF lab is borrowed from tissue culture discipline. Unlike other field of biomedicine, the technical jump in innovation is rather slow, with regard to automation in IVF lab. There is a huge scope of robotics or automation in IVF lab systems. The process is very complex and needs high level of accuracy and errors close to zero, along with proper documentation. Embryo manipulations/intracytoplasmic sperm injection, cryopreservation, culture systems, etc. can be automated when right minds come together from embryology, automation engineering, and IT professionals. The artificial neural network (ANN) system in the near future can act as a routine information technology platform for the IVF unit and capable of recalling and evaluating a vast amount of information in a rapid and automated manner to provide an objective indication on the outcome of an IVF cycle. ANNs are an exceptional candidate in providing the fertility specialist with numerical estimates to promote personalization of healthcare and adaptation of the course of treatment according to the indications.

Keywords

Automation Robotics Artificial neural network Artificial intelligence Embryo selection Vitrification Microfluidics 

References

  1. 1.
    IFFS Global Reproductive Health Surveillance. International Federation of Fertility Societies. 2016;1(1). http://journals.lww.com/grh/Fulltext/2016/09000/IFFS_Surveillance_2016.1.aspx.
  2. 2.
    Faddy MJ, et al. A demographic projection of the contribution of assisted reproductive technologies to world population growth. Reprod Biomed Online. 2017;36(4):455–8.CrossRefGoogle Scholar
  3. 3.
    Svalander P, Tucker M. The IVF laboratory; a historical perspective. In: Varghese AC, Sjoblom P, Jayaprakasan K, editors. A practical guide to setting up an IVF lab, embryo culture systems and running the unit. India: Jaypee Brothers Medical Publishers; 2013. p. 1–12.Google Scholar
  4. 4.
    Meseguer M, Kruhne U, Laursen S. Full in vitro fertilization laboratory mechanization: toward robotic assisted reproduction? Fertil Steril. 2012;97(6):1277–86.CrossRefGoogle Scholar
  5. 5.
    Siristatidis CS, Chrelias C, Pouliakis A, Katsimanis E, Kassanos D. Artificial neural networks in gynaecological diseases: current and potential future applications. Med Sci Monit. 2010;16(10):RA231–6.PubMedGoogle Scholar
  6. 6.
    Siristatidis C, Pouliakis A, Chrelias C, Kassanos D. Artificial intelligence in IVF: a need. Syst Biol Reprod Med. 2011;57(4):179–85.CrossRefGoogle Scholar
  7. 7.
    Kaufmann SJ, Eastaugh JL, Snowden S, Smye SW, Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Hum Reprod. 1997;12(7):1454–7. PMID: 9262277.CrossRefGoogle Scholar
  8. 8.
    Edwards RG, Purdy JM, Steptoe PC, Walters DE. The growth of human preimplantation embryos in vitro. Am J Obstet Gynecol. 1981;141:408–16.CrossRefGoogle Scholar
  9. 9.
    Alpha Scientists in Reproductive Medicine and ESHRE Special Interest Group of Embryology. The Istanbul consensus workshop on embryo assessment: proceedings of an expert meeting. Hum Reprod. 2011;26:1270–83.CrossRefGoogle Scholar
  10. 10.
    Palmer GA, Traeger-Synodinos J, Davies S, Tzetis M, Vrettou C, Mastrominas M, et al. Pregnancies following blastocyst stage transfer in PGD cycles at risk for beta-thalassaemic haemoglobinopathies. Hum Reprod. 2002;17:25–31.CrossRefGoogle Scholar
  11. 11.
    Mastenbroek S, Twisk M, van der Veen F, Repping S. Preimplantation genetic screening: a systematic review and metaanalysis of RCTs. Hum Reprod Update. 2011;17:454–66.CrossRefGoogle Scholar
  12. 12.
    Nel-Themaat L, Nagy ZP. A review of the promises and pitfalls of oocyte and embryo metabolomics. Placenta. 2011;32(Suppl 3):S257–63.CrossRefGoogle Scholar
  13. 13.
    Swain JE. Could time-lapse embryo imaging reduce the need for biopsy and PGS? J Assist Reprod Genet. 2013;30:1081–90.CrossRefGoogle Scholar
  14. 14.
    Anifandis G. Temperature variations inside commercial IVF incubators. J Assist Reprod Genet. 2013;30:1587–8.CrossRefGoogle Scholar
  15. 15.
    Chen AA, Tan L, Suraj V, Reijo Pera R, Shen S. Biomarkers identified with time-lapse imaging: discovery, validation, and practical application. Fertil Steril. 2013;99:1035–43.CrossRefGoogle Scholar
  16. 16.
    Lemmen JG, AgerholmI ZS. Kineticmarkers of human embryo quality using time-lapse recordings of IVF/ICSI-fertilized oocytes. Reprod Biomed Online. 2008;17:385–91.CrossRefGoogle Scholar
  17. 17.
    Athayde Wirka K, Chen AA, Conaghan J, Ivani K, Gvakharia M, Behr B, et al. Atypical embryo phenotypes identified by time-lapse microscopy: high prevalence and association with embryo development. Fertil Steril. 2014;101:1637–48.CrossRefGoogle Scholar
  18. 18.
    Hardarson T, Löfman C, Coull G, Sjögren A, Hamberger L, Edwards RG. Internalization of cellular fragments in a human embryo: timelapse recordings. Reprod Biomed Online. 2002;5:36–8.CrossRefGoogle Scholar
  19. 19.
    Rubio I, Kuhlmann R, Agerholm I, Kirk J, Herrero J, Escriba MJ, et al. Limited implantation success of direct-cleaved human zygotes: a time-lapse study. Fertil Steril. 2012;98:1458–63.CrossRefGoogle Scholar
  20. 20.
    Siristatidis C, Komitopoulou MA, Makris A, et al. Morphokinetic parameters of early embryo development via time lapse monitoring and their effect on embryo selection and ICSI outcomes: a prospective cohort study. J Assist Reprod Genet. 2015;32(4):563–70.CrossRefGoogle Scholar
  21. 21.
    Wong CC, Loewke KE, Bossert NL, Behr B, De Jonge CJ, Baer TM, et al. Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat Biotechnol. 2010;28:1115–21.CrossRefGoogle Scholar
  22. 22.
    Hlinka D, Kaľatová B, Uhrinová I, Dolinská S, Rutarová J, Rezáčová J, et al. Time-lapse cleavage rating predicts human embryo viability. Physiol Res. 2012;61:513–25.Google Scholar
  23. 23.
    Campbell A, Fishel S, Bowman N, Duffy S, Sedler M, Thornton S. Retrospective analysis of outcomes after IVF using an aneuploidy risk model derived from time-lapse imaging without PGS. Reprod Biomed Online. 2013;27:140–6.CrossRefGoogle Scholar
  24. 24.
    Insua MF, Cobo AC, Larreategui Z, Ferrando M, Serra V, Meseguer M. Obstetric and perinatal outcomes of pregnancies conceived with embryos cultured in a time-lapse monitoring system. Fertil Steril. 2017;108(3):498–504.CrossRefGoogle Scholar
  25. 25.
    Kaser DJ, Bormann CL, Missmer SA, Farland LV, Ginsburg ES, Racowsky C. A pilot randomized controlled trial of Day 3 single embryo transfer with adjunctive time-lapse selection versus Day 5 single embryo transfer with or without adjunctive time-lapse selection. Hum Reprod. 2017;32(8):1598–603.  https://doi.org/10.1109/ICRA.2015.7139562.CrossRefPubMedGoogle Scholar
  26. 26.
    Kieslinger DC, De Gheselle S, Lambalk CB, De Sutter P, Hanna Kostelijk E, Twisk JWR, van Rijswijk J, Van den Abbeel E, Vergouw CG. Embryo selection using time-lapse analysis (Early Embryo Viability Assessment) in conjunction with standard morphology: a prospective two-center pilot study. Hum Reprod. 2016;31(11):2450–7.CrossRefGoogle Scholar
  27. 27.
    Armstrong S, Arroll N, Cree LM, Jordan V, Farquhar C. Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst Rev. 2015;(2):CD011320.Google Scholar
  28. 28.
    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.CrossRefGoogle Scholar
  29. 29.
    Polanski LT, Coelho Neto MA, Nastri CO, Navarro PA, Ferriani A, Raine-Fenning N, Martins WP. Time-lapse embryo imaging for improving reproductive outcomes: systematic review and meta-analysis. Ultrasound Obstet Gynecol. 2014;44(4):394–401.CrossRefGoogle Scholar
  30. 30.
    Racowsky C, Kovacs P, Martins WP. A critical appraisal of time-lapse imaging for embryo selection: where are we and where do we need to go? J Assist Reprod Genet. 2015;32(7):1025–30.CrossRefGoogle Scholar
  31. 31.
    Yang L, Cai S, Zhang S, Kong X, Gu Y, Lu C, Dai J, Gong F, Lu G, Lin G. Single embryo transfer by Day 3 time-lapse selection versus Day 5 conventional morphological selection: a randomized, open-label, non-inferiority trial. Hum Reprod. 2018;33:869.  https://doi.org/10.1093/humrep/dey047.CrossRefPubMedGoogle Scholar
  32. 32.
    Aparicio-Ruiz B, Basile N, Pérez Albalá S, Bronet F, Remohí J, Meseguer M. Automatic time-lapse instrument is superior to single-point morphology observation for selecting viable embryos: retrospective study in oocyte donation. Fertil Steril. 2016;106(6):1379–85.e10.  https://doi.org/10.1016/j.fertnstert.2016.07.1117.CrossRefGoogle Scholar
  33. 33.
    Fishel S, Campbell A, Montgomery S, Smith R, Nice L, Duffy S, Jenner L, Berrisford K, Kellam L, Smith R, D’Cruz I, Beccles A. Live births after embryo selection using morphokinetics versus conventional morphology: a retrospective analysis. Reprod Biomed Online. 2017;35(4):407–16.CrossRefGoogle Scholar
  34. 34.
    Teixeira DM, Barbosa MAP, Ferriani RA, Navarro PA, Raine-Fenning N, Nastri CO, Martins WP. Regular (ICSI) versus ultra-high magnification (IMSI) sperm selection for assisted reproduction. Cochrane Database Syst Rev. 2013;7:CD010167.Google Scholar
  35. 35.
    Mirsky SK, Barnea I, Shaked NT. Label-free quantitative imaging of sperm for in vitro fertilization using interferometric phase microscopy. J Fertil In Vitro IVF Worldw Reprod Med Genet Stem Cell Biol. 2016;4:190.  https://doi.org/10.4172/2375-4508.1000190.CrossRefGoogle Scholar
  36. 36.
    De Angelis A, Managò S, Ferrara MA, Napolitano M, Coppola G, De Luca AC. Combined Raman spectroscopy and digital holographic microscopy for sperm cell quality analysis. J Spectrosc. 2017;2017, Article ID 9876063, 14 pages.Google Scholar
  37. 37.
    Huang Z, Chen G, Chen X, Wang J, Chen J, Lu P, Chen R. Rapid and label-free identification of normal spermatozoa based on image analysis and micro-Raman spectroscopy. J Biophotonics. 2014;7:671–5.CrossRefGoogle Scholar
  38. 38.
    Heraud P, Marzec KM, Zhang QH, Yuen WS, Carroll J, Wood BR. Label-free in vivo Raman microspectroscopic imaging of the macromolecular architecture of oocytes. Sci Rep. 2017;7:8945.  https://doi.org/10.1038/s41598-017-08973-0.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Rusciano G, De Canditiis C, Zito G, et al. Raman-microscopy investigation of vitrification-induced structural damages in mature bovine oocytes. Lee JR, ed. PLoS One. 2017;12(5):e0177677.  https://doi.org/10.1371/journal.pone.0177677.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Bogliolo L, Murrone O, Di Emidio G, et al. Raman spectroscopy-based approach to detect aging-related oxidative damage in the mouse oocyte. J Assist Reprod Genet. 2013;30(7):877–82.  https://doi.org/10.1007/s10815-013-0046-6.CrossRefPubMedPubMedCentralGoogle Scholar
  41. 41.
    Li X, Xu Y, Fu J, Zhang W-B, Liu S-Y, Sun X-X. Non-invasive metabolomic profiling of embryo culture media and morphology grading to predict implantation outcome in frozen-thawed embryo transfer cycles. J Assist Reprod Genet. 2015;32(11):1597–605.  https://doi.org/10.1007/s10815-015-0578-z.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Scott R, Seli E, Miller K, Sakkas D, Scott K, Burns DH. Noninvasive metabolomic profiling of human embryo culture media using Raman spectroscopy predicts embryonic reproductive potential: a prospective blinded pilot study. Fertil Steril. 2008;90(1):77–83.  https://doi.org/10.1016/j.fertnstert.2007.11.058.. Epub 2008 Feb 20.CrossRefGoogle Scholar
  43. 43.
    Siristatidis CS, Sertedaki E, Vaidakis D, Varounis C, Trivella M. Metabolomics for improving pregnancy outcomes in women undergoing assisted reproductive technologies. Cochrane Database Syst Rev. 2018;3:CD011872.  https://doi.org/10.1002/14651858.CD011872.pub3.CrossRefPubMedGoogle Scholar
  44. 44.
    Ishigaki M, Hashimoto K, Sato H, Ozaki Y. Non-destructive monitoring of mouse embryo development and its qualitative evaluation at the molecular level using Raman spectroscopy. Sci Rep. 2017;7:43942.  https://doi.org/10.1038/srep43942.CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Tejera A, Castello D, de Los Santos JM, Pellicer A, Remohi J, Meseguer M. Combination of metabolism measurement and a time-lapse system provides an embryo selection method based on oxygen uptake and chronology of cytokinesis timing. Fertil Steril. 2016;106:119–26 e2.CrossRefGoogle Scholar
  46. 46.
    Pyne DG, Liu J, Abdelgawad M, Sun Y. Digital microfluidic processing of mammalian embryos for vitrification. PLoS One. 2014;9:e108128.CrossRefGoogle Scholar
  47. 47.
    Giglio A, Cheong SH, Neri QV, Rosenwaks Z, Palermo GD. ICSI-on-a-chip. Fertil Steril. 2013;100(3):S479.CrossRefGoogle Scholar
  48. 48.
    Perozziello G, Mollenbach J, Laursen S, Di Fabrizio E, Gernaey K, Kruhne U. Lab on a chip automates in vitro cell culturing. Microelectron Eng. 2012;98:655–8.CrossRefGoogle Scholar
  49. 49.
    Roy TK, Brandi S, Peura TT. Chapter 20 gavi-automated vitrification instrument. In: Nagy Z, Varghese A, Agarwal A, editors. Cryopreservation of mammalian gametes and embryos. Methods in molecular biology, vol. 1568. New York: Humana Press; 2017.Google Scholar
  50. 50.
    Liu J, et al. Automated robotic vitrification of embryos, 2015 IEEE Int Conf Robot Autom (ICRA), Seattle, WA, 2015, p. 2685–90.Google Scholar
  51. 51.
    Arav A, et al. A new, simple, automatic vitrification device: preliminary results with mice and bovine oocytes and embryos. JARG J Assist Reprod Genet. 2018;35:1161–8.. (In press).CrossRefGoogle Scholar
  52. 52.
    Hyslop L, Prathalingam N, Nowak L, et al. A novel isolator-based system promotes viability of human embryos during laboratory processing. Singh SR, ed. PLoS One. 2012;7(2):e31010.  https://doi.org/10.1371/journal.pone.0031010.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Chrelias C, Siristatidis C, Kassanos D. Wavelet analysis and neural networks for intrapartum fetal monitoring. Can we long for a new technique? Is it doable? Med Sci Monit. 2008;14(1):LE1.PubMedGoogle Scholar
  54. 54.
    Makris GM, Pouliakis A, Siristatidis C, Margari N, Terzakis E, Koureas N, Pergialiotis V, Papantoniou N, Karakitsos P. Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions. Diagn Cytopathol. 2017;45(3):202–11.CrossRefGoogle Scholar
  55. 55.
    Siristatidis C, Vogiatzi P, Pouliakis A, Trivella M, Papantoniou N, Bettocchi S. Predicting IVF outcome: a proposed web-based system using artificial Intelligence. In Vivo. 2016;30(4):507–12.PubMedGoogle Scholar
  56. 56.
    McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys. 1943;5:115–33.CrossRefGoogle Scholar
  57. 57.
    Haykin S. Neural networks a comprehensive foundation. New York: Macmillan College Publishing Company; 1994.Google Scholar
  58. 58.
    Kohonen T. Self-organization and associative memory. 3rd ed. New York: Springer; 1988.CrossRefGoogle Scholar
  59. 59.
    Swingler K. Applying neural networks: a practical guide. 3rd ed. San Francisco: Academic Press; 2001. p. 109.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alex C. Varghese
    • 1
  • Charalampos S. Siristatidis
    • 2
  1. 1.Astra Fertility GroupMississaugaCanada
  2. 2.Assisted Reproduction Unit, “Attikon” Hospital, Medical School, National and Kapodistrian University of AthensAthensGreece

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