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Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018

  • Assisted Reproduction Technologies
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Abstract

Sixteen artificial intelligence (AI) and machine learning (ML) approaches were reported at the 2018 annual congresses of the American Society for Reproductive Biology (9) and European Society for Human Reproduction and Embryology (7). Nearly every aspect of patient care was investigated, including sperm morphology, sperm identification, identification of empty or oocyte containing follicles, predicting embryo cell stages, predicting blastocyst formation from oocytes, assessing human blastocyst quality, predicting live birth from blastocysts, improving embryo selection, and for developing optimal IVF stimulation protocols. This represents a substantial increase in reports over 2017, where just one abstract each was reported at ASRM (AI) and ESHRE (ML). Our analysis reveals wide variability in how AI and ML methods are described (from not at all or very generic to fully describing the architectural framework) and large variability on accepted dataset sizes (from just 3 patients with 16 follicles in the smallest dataset to 661,060 images of 11,898 human embryos in one of the largest). AI and ML are clearly burgeoning methodologies in human reproduction and embryology and would benefit from early application of reporting standards.

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References

  1. Scientific Congress Supplement: Oral and Poster Session Abstracts. Fertil Steril. 2018;110(4):Supplement e1–e468

  2. Scientific Congress Supplement: Oral and Poster Session Abstracts. Fertil Steril. 2017;108(3):Supplement e1–e422

  3. Abstracts of the 33rd Annual Meeting of the European Society of Human Reproduction and Embryology. Hum Reprod. 2017;32(Supplemental 1):i1–i539

  4. Abstracts of the 34rd Annual Meeting of the European Society of Human Reproduction and Embryology. Hum Reprod. 2018;33(Supplemental 1):i1–i541

  5. European IVF-Monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology (ESHRE), Calhaz-Jorge C, de Geyter C, Kupka MS, de Mouzon J, Erb K, et al. Assisted reproductive technology in Europe, 2012: results generated from European registers by ESHRE. Hum Reprod. 2016;31(8):1638–52.

    Article  Google Scholar 

  6. 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.

    Article  CAS  PubMed  Google Scholar 

  7. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285–98.

    Article  PubMed  Google Scholar 

  8. Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18(4):570–84.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ng K, Steinhubl SR, deFilippi C, Dey S, Stewart WF. Early detection of heart failure using electronic health records: practical implications for time before diagnosis, data diversity, data quantity, and data density. Circ Cardiovasc Qual Outcomes. 2016;9(6):649–58.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Bringing Precision Medicine to Community Oncologists. Cancer Discov. 2017;7(1):6–7

  12. 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.

    PubMed  Google Scholar 

  13. Meseguer M, Kruhne U, Laursen S. Full in vitro fertilization laboratory mechanization: toward robotic assisted reproduction? Fertil Steril. 2012;97(6):1277–86.

    Article  PubMed  Google Scholar 

  14. 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.

    PubMed  Google Scholar 

  15. Siristatidis C, Pouliakis A, Chrelias C, Kassanos D. Artificial intelligence in IVF: a need. Syst Biol Reprod Med. 2011;57(4):179–85.

    Article  PubMed  Google Scholar 

  16. Milewski R, Milewska AJ, Więsak T, Morgan A. Comparison of Artificial Neural Networks and Logistic Regression Analysis in Pregnancy Prediction Using the In Vitro Fertilization Treatment. Stud Logic Grammar Rhetoric. 2013;35(48):39–48.

    Article  Google Scholar 

  17. Almeida JL, Cole KD, Plant AL. Standards for cell line authentication and beyond. PLoS Biol. 2016;14(6):e1002476.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Helsby MA, Fenn JR, Chalmers AD. Reporting research antibody use: how to increase experimental reproducibility. F1000Res. 2013;2:153.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Li MM, Datto M, Duncavage EJ, Kulkarni S, Lindeman NI, Roy S, et al. Standards and guidelines for the interpretation and reporting of sequence variants in Cancer: a joint consensus recommendation of the Association for Molecular Pathology, American Society of Clinical Oncology, and College of American Pathologists. J Mol Diagn. 2017;19(1):4–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res. 2016;18(12):e323.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Beleites C, Neugebauer U, Bocklitz T, Krafft C, Popp J. Sample size planning for classification models. Anal Chim Acta. 2013;760:25–33.

    Article  CAS  PubMed  Google Scholar 

  22. Capalbo A, Rienzi L, Cimadomo D, Maggiulli R, Elliott T, Wright G, et al. Correlation between standard blastocyst morphology, euploidy and implantation: an observational study in two centers involving 956 screened blastocysts. Hum Reprod. 2014;29(6):1173–81.

    Article  PubMed  Google Scholar 

  23. 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(10):1115–21.

    Article  CAS  PubMed  Google Scholar 

  24. 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.

    Article  PubMed  Google Scholar 

  25. Kirkegaard K, Agerholm IE, Ingerslev HJ. Time-lapse monitoring as a tool for clinical embryo assessment. Hum Reprod. 2012;27(5):1277–85.

    Article  PubMed  Google Scholar 

  26. Rubio I, Galán A, Larreategui Z, Ayerdi F, Bellver J, Herrero J, et al. Clinical validation of embryo culture and selection by morphokinetic analysis: a randomized, controlled trial of the EmbryoScope. Fertil Steril. 2014;102(5):1287–1294 e5.

    Article  PubMed  Google Scholar 

  27. Cicconet M, Gutwein M, Gunsalus KC, Geiger D. Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development. Comput Biol Med. 2014;51:24–34.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Basile N, Vime P, Florensa M, Aparicio Ruiz B, García Velasco JA, Remohí J, et al. The use of morphokinetics as a predictor of implantation: a multicentric study to define and validate an algorithm for embryo selection. Hum Reprod. 2015;30(2):276–83.

    Article  CAS  PubMed  Google Scholar 

  29. Tian Y, Yin YB, Duan FQ, Wang WZ, Wang W, Zhou MQ. Automatic blastomere recognition from a single embryo image. Comput Math Methods Med. 2014;2014:628312.

    PubMed  PubMed Central  Google Scholar 

  30. Santos Filho E, et al. A method for semi-automatic grading of human blastocyst microscope images. Hum Reprod. 2012;27(9):2641–8.

    Article  CAS  PubMed  Google Scholar 

  31. Barrie A, Homburg R, McDowell G, Brown J, Kingsland C, Troup S. Examining the efficacy of six published time-lapse imaging embryo selection algorithms to predict implantation to demonstrate the need for the development of specific, in-house morphokinetic selection algorithms. Fertil Steril. 2017;107(3):613–21.

    Article  PubMed  Google Scholar 

  32. Diamond MP, Suraj V, Behnke EJ, Yang X, Angle MJ, Lambe-Steinmiller JC, et al. 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. 2015;32(1):61–8.

    Article  PubMed  Google Scholar 

  33. 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 

  34. Armstrong S, Bhide P, Jordan V, Pacey A, Farquhar C. Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst Rev. 2018;5:CD011320.

    PubMed  Google Scholar 

  35. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Rocha JC, Passalia FJ, Matos FD, Takahashi MB, Ciniciato DS, Maserati MP, et al. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Sci Rep. 2017;7(1):7659.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Dimitriadis I, Christou G, Dickinson K, McLellan S, Brock M, Souter I, et al. Cohort embryo selection (CES): a quick and simple method for selecting cleavage stage embryos that will become high quality blastocysts (HQB). Fertil Steril. 2017;108(3):e162–3.

    Article  Google Scholar 

  38. Gleicher N, Kushnir VA, Barad DH. How PGS/PGT-A laboratories succeeded in losing all credibility. Reprod BioMed Online. 2018;37(2):242–5.

    Article  PubMed  Google Scholar 

  39. Grati FR, Gallazzi G, Branca L, Maggi F, Simoni G, Yaron Y. Response: how PGS/PGT-A laboratories succeeded in losing all credibility. Reprod BioMed Online. 2018;37(2):246.

    Article  PubMed  Google Scholar 

  40. Munne S, et al. Response: how PGS/PGT-a laboratories succeeded in losing all credibility. Reprod BioMed Online. 2018;37(2):247–9.

    Article  PubMed  Google Scholar 

  41. Penzias AS. Recurrent IVF failure: other factors. Fertil Steril. 2012;97(5):1033–8.

    Article  PubMed  Google Scholar 

  42. Verpoest W, Staessen C, Bossuyt PM, Goossens V, Altarescu G, Bonduelle M, et al. Preimplantation genetic testing for aneuploidy by microarray analysis of polar bodies in advanced maternal age: a randomized clinical trial. Hum Reprod. 2018;33(9):1767–76.

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. San Diego Fertility Center, 11425 El Camino Real, San Diego, CA, 92130, USA

    Carol Lynn Curchoe

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

    Charles L. Bormann

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  1. Carol Lynn Curchoe
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  2. Charles L. Bormann
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Correspondence to Carol Lynn Curchoe.

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Curchoe, C.L., Bormann, C.L. Artificial intelligence and machine learning for human reproduction and embryology presented at ASRM and ESHRE 2018. J Assist Reprod Genet 36, 591–600 (2019). https://doi.org/10.1007/s10815-019-01408-x

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  • DOI: https://doi.org/10.1007/s10815-019-01408-x

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