Opportunities and limitations of introducing artificial intelligence technologies into reproductive medicine

Given the increasing problem of infertility in the Russian Federation, assisted reproductive technologies (ART) have proven to be one of the most effective treatments for this condition. Notably, the introduction of ART methods, particularly in vitro fertilization (IVF), has led to markedly increased birth rates over the past two decades. Studies show that machine learning algorithms can process images of embryos to assess their quality, thus facilitating the selection of the most viable among them for transfer. There are ethical and technical barriers hindering the widespread adoption of artificial intelligence (AI) in clinical practice, including concerns over data privacy as well as a need to train specialists to deal with new technologies. AI can analyze vast amounts of data, including medical histories and research results, to more accurately predict pregnancy outcomes. This enables doctors to make more justified clinical decisions. In the future, AI algorithms will be able to analyze patient data more efficiently, helping to identify the causes of infertility at earlier stages.

1. Shah P.K., Gher J.M. Human rights approaches to reducing infertility. Int J Gynaecol Obstet. 2023;162(1):368–74. https://doi.org/10.1002/ijgo.14878.

2. Korsak V.S., Smirnova A.A., Shurygina O.V.. ART Register of RAHR, 2019. [Registr VRT Rossijskoj associacii reprodukcii cheloveka. Otchet za 2019 god]. Problemy reprodukcii. 2021;27(6):14–29. (In Russ.). https://doi.org/10.17116/repro20212706114.

3. Villani M.T., Morini D., Spaggiari G. et al. Are sperm parameters able to predict the success of assisted reproductive technology? A retrospective analysis of over 22,000 assisted reproductive technology cycles. Andrology. 2022;10(2):310–21. https://doi.org/10.1111/andr.13123.

4. Louis C.M., Erwin A., Handayani N. et al. Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF. J Assist Reprod Genet. 2021;38(7):1627–39. https://doi.org/10.1007/s10815-021-02123-2.

5. Khosravi P., Kazemi E., Zhan Q. et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit Med. 2019;2:21. https://doi.org/10.1038/s41746-019-0096-y.

6. Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. et al. Artificial intelligence in healthcare and medicine: the history of key events, its significance for doctors, the level of development in different countries. [Iskusstvennyj intellekt v zdravoohranenii i medicine: istoriya klyuchevyh sobytij, ego znachimost' dlya vrachej, uroven' razvitiya v raznyh stranah]. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2024;17(2):243–50. (In Russ.). https://doi.org/10.17749/2070-4909/farmakoekonomika.2024.254.

7. Meldo A.A., Utkin L.V., Trofimova T.N. Artificial intelligence in medicine: current state and main directions of development of the intellectual diagnostics. [Iskusstvennyj intellekt v medicine: sovremennoe sostoyanie i osnovnye napravleniya razvitiya intellektual'noj diagnostiki]. Luchevaya diagnostika i terapiya. 2020;11(1):9–17. (In Russ.). https://doi.org/10.22328/2079-5343-2020-11-1-9-17.

8. Adamyan L.V. The use of artificial intelligence in reproductive medicine. [Ispol'zovanie iskusstvennogo intellekta v reproduktivnoj medicine]. Problemy reprodukcii. 2021;27(3):6–13. (In Russ.). https://doi.org/10.17116/repro2021270316.

9. Hanassab S., Abbara A., Yeung A.C. et al. The prospect of artificial intelligence to personalize assisted reproductive technology. NPJ Digit Med. 2024;7(1):55. https://doi.org/10.1038/s41746-024-01006-x.

10. Jiang V.S., Pavlovic Z.J., Hariton E. The role of artificial intelligence and machine learning in assisted reproductive technologies. Obstet Gynecol Clin North Am. 2023;50(4):747–62. https://doi.org/10.1016/j.ogc.2023.09.003.

11. Bormann C.L., Kanakasabapathy M.K., Thirumalaraju P. et al. Performance of a deep learning based neural network in the selection of human blastocysts for implantation. Elife. 2020;9:e55301. https://doi.org/10.7554/eLife.55301.

12. Curchoe C.L., Bormann C., Hammond E. et al. Assuring quality in assisted reproduction laboratories: assessing the performance of ART Compass – a digital art staff management platform. J Assist Reprod Genet. 2023;40(2):265–78. https://doi.org/10.1007/s10815-023-02713-2.

13. Jiang V.S., Bormann C.L. Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertil Steril. 2023;120(1):17–23. https://doi.org/10.1016/j.fertnstert.2023.05.149.

14. Wang R., Pan W., Jin L. et al. Artificial intelligence in reproductive medicine. Reproduction. 2019;158(4):R139–R154. https://doi.org/10.1530/REP-18-0523.

15. Ivshin A.A., Bagaudin T.Z., Gusev A.V. Artificial intelligence on guard of reproductive health. [Iskusstvennyj intellekt na strazhe reproduktivnogo zdorov'ya]. Akusherstvo i ginekologiya. 2021;(5):17–24. (In Russ.). https://doi.org/10.18565/aig.2021.5.17-24.

16. Coelho Neto M.A., Ludwin A., Borrell A. et al. Counting ovarian antral follicles by ultrasound: a practical guide. Ultrasound Obstet Gynecol. 2018;51(1):10–20. https://doi.org/10.1002/uog.18945.

17. Li H., Fang J., Liu S. et al. CR-Unet: A Composite Network for Ovary and Follicle Segmentation in Ultrasound Images. IEEE J Biomed Health Inform. 2020;24(4):974–83. https://doi.org/10.1109/JBHI.2019.2946092.

18. Mathur P., Kakwani K., Kudavelly S., Ramaraju G.A. Deep learning based quantification of ovary and follicles using 3D transvaginal ultrasound in assisted reproduction. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:2109–12. https://doi.org/10.1109/EMBC44109.2020.9176703.

19. Yang X., Li H., Wang Y. et al. Contrastive rendering with semi-supervised learning for ovary and follicle segmentation from 3D ultrasound. Med Image Anal. 2021;73:102134. https://doi.org/10.1016/j.media.2021.102134.

20. Liang X., Liang J., Zeng F. et al. Evaluation of oocyte maturity using artificial intelligence quantification of follicle volume biomarker by three-dimensional ultrasound. Reprod Biomed Online. 2022;45(6):1197–206. https://doi.org/10.1016/j.rbmo.2022.07.012.

21. Noor N., Vignarajan C.P., Malhotra N., Vanamail P. Three-dimensional automated volume calculation (sonography-based automated volume count) versus two-dimensional manual ultrasonography for follicular tracking and oocyte retrieval in women undergoing in vitro fertilization-embryo transfer: a randomized controlled trial. J Hum Reprod Sci. 2020;13(4):296–302. https://doi.org/10.4103/jhrs.JHRS_91_20.

22. Andreeva E.A., Khonina N.A., Pasman N.M., Chernykh E.R. Сytokins in the regulation of ovarian folliculogenesis (a review). [Citokiny v regulyacii ovarial'nogo follikulogeneza (obzor literatury)]. Problemy reprodukcii. 2017;23(1):8–14. (In Russ.). https://doi.org/10.17116/repro20172318-14.

23. Manna C., Nanni L., Lumini A., Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reprod Biomed Online. 2013;26(1):42–9. https://doi.org/10.1016/j.rbmo.2012.09.015.

24. 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.

25. Fjeldstad J., Qi W., Mercuri N. et al. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reprod Biomed Online. 2024;48(6):103842. https://doi.org/10.1016/j.rbmo.2024.103842.

26. Boylan C.F., Sambo K.M., Neal-Perry G., Brayboy L.M. Ex ovo omnia-why don't we know more about egg quality via imaging? Biol Reprod. 2024;110(6):1201–12. https://doi.org/10.1093/biolre/ioae080.

27. Alper M.M., Fauser B.C. Ovarian stimulation protocols for IVF: is more better than less? Reprod Biomed Online. 2017;34(4):345–53. https://doi.org/10.1016/j.rbmo.2017.01.010.

28. Glujovsky D., Pesce R., Miguens M. et al. How effective are the non-conventional ovarian stimulation protocols in ART? A systematic review and meta-analysis. J Assist Reprod Genet. 2020;37(12):2913–28. https://doi.org/10.1007/s10815-020-01966-5.

29. Mol B.W., Bossuyt P.M., Sunkara S.K. et al. Personalized ovarian stimulation for assisted reproductive technology: study design considerations to move from hype to added value for patients. Fertil Steril. 2018;109(6):968–79. https://doi.org/10.1016/j.fertnstert.2018.04.037.

30. Doroftei B., Ilie O.D., Anton N. et al. A narrative review discussing the efficiency of personalized dosing algorithm of follitropin delta for ovarian stimulation and the reproductive and clinical outcomes. Diagnostics. 2023;13(2):177. https://doi.org/10.3390/diagnostics13020177.

31. Haahr T., Esteves S.C., Humaidan P. Individualized controlled ovarian stimulation in expected poor-responders: an update. Reprod Biol Endocrinol. 2018;16(1):20. https://doi.org/10.1186/s12958-018-0342-1.

32. Hariton E., Pavlovic Z., Fanton M., Jiang V.S. Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes. Fertil Steril. 2023;120(1):8–16. https://doi.org/10.1016/j.fertnstert.2023.05.148.

33. AlSaad R., Abd-Alrazaq A., Choucair F. et al. Harnessing artificial intelligence to predict ovarian stimulation outcomes in in vitro fertilization: scoping review. J Med Internet Res. 2024;26:e53396. https://doi.org/10.2196/53396.

34. 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. 2019;36(4):591–600. https://doi.org/10.1007/s10815-019-01408-x.

35. Siristatidis C., Stavros S., Drakeley A. et al. Omics and artificial intelligence to improve in vitro fertilization (IVF) success: a proposed protocol. Diagnostics. 2021;11(5):743. https://doi.org/10.3390/diagnostics11050743.

36. Mann M., Kumar C., Zeng W.F., Strauss M.T. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 2021;12(8):759–70. https://doi.org/10.1016/j.cels.2021.06.006.

37. Zieliński K., Pukszta S., Mickiewicz M. et al. Personalized prediction of the secondary oocytes number after ovarian stimulation: a machine learning model based on clinical and genetic data. PLoS Comput Biol. 2023;19(4):e1011020. https://doi.org/10.1371/journal.pcbi.1011020.

38. Letterie G., MacDonald A., Shi Z. An artificial intelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions. Reprod Biomed Online. 2022;44(2):254–60. https://doi.org/10.1016/j.rbmo.2021.10.006.

39. Cesario A., D'Oria M., Bove F. et al. Personalized clinical phenotyping through systems medicine and artificial intelligence. J Pers Med. 2021;11(4):265. https://doi.org/10.3390/jpm11040265.

40. Ferrand T., Boulant J., He C. et al. Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Hum Reprod. 2023;38(10):1918–26. https://doi.org/10.1093/humrep/dead163.

41. Simopoulou M., Sfakianoudis K., Maziotis E. et al. Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence. J Assist Reprod Genet. 2018;35(9):1545–57. https://doi.org/10.1007/s10815-018-1266-6.

42. Basile N., Elkhatib I., Meseguer M. A strength, weaknesses, opportunities and threats analysis on time lapse. Curr Opin Obstet Gynecol. 2019;31(3):148–55. https://doi.org/10.1097/GCO.0000000000000534.

43. Zaninovic N., Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril. 2020;114(5):914–20. https://doi.org/10.1016/j.fertnstert.2020.09.157.

44. Capalbo A., Rienzi L., Cimadomo D. 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. https://doi.org/10.1093/humrep/deu033.

45. Gardner D.K., Meseguer M., Rubio C., Treff N.R. Diagnosis of human preimplantation embryo viability. Hum Reprod Update. 2015;21(6):727–47. https://doi.org/10.1093/humupd/dmu064.

46. Rubio I., Galán A., Larreategui Z. 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. https://doi.org/10.1016/j.fertnstert.2014.07.738.

47. Meng Q., Xu Y., Zheng A. et al. Noninvasive embryo evaluation and selection by time-lapse monitoring vs. conventional morphologic assessment in women undergoing in vitro fertilization/intracytoplasmic sperm injection: a single-center randomized controlled study. Fertil Steril. 2022;117(6):1203–12. https://doi.org/10.1016/j.fertnstert.2022.02.015.

48. Fruchter-Goldmeier Y., Kantor B., Ben-Meir A. et al. An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential. Sci Rep. 2023;13(1):14617. https://doi.org/10.1038/s41598-023-40923-x.

49. Siristatidis C., Pouliakis A., Chrelias C., Kassanos D. Artificial intelligence in IVF: a need. Syst Biol Reprod Med. 2011;57(4):179–85. https://doi.org/10.3109/19396368.2011.558607.

50. Bori L., Paya E., Alegre L. et al. Novel and conventional embryo parameters as input data for artificial neural networks: an artificial intelligence model applied for prediction of the implantation potential. Fertil Steril. 2020;114(6):1232–41. https://doi.org/10.1016/j.fertnstert.2020.08.023.

51. Fordham D.E., Rosentraub D., Polsky A.L. et al. Embryologist agreement when assessing blastocyst implantation probability: is data-driven prediction the solution to embryo assessment subjectivity? Hum Reprod. 2022;37(10):2275–90. https://doi.org/10.1093/humrep/deac171.

52. Milewski R., Kuczyńska A., Stankiewicz B., Kuczyński W. How much information about embryo implantation potential is included in morphokinetic data? A prediction model based on artificial neural networks and principal component analysis. Adv Med Sci. 2017;62(1):202–6. https://doi.org/10.1016/j.advms.2017.02.001.

53. Liu X., Lou H., Zhang J. et al. Clinical outcome analysis of frozen-thawed embryo transfer on Day 7. Front Endocrinol. 2022;13:1082597. https://doi.org/10.3389/fendo.2022.1082597.

54. Canosa S., Licheri N., Bergandi L. et al. A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development. J Ovarian Res. 2024;17(1):63. https://doi.org/10.1186/s13048-024-01376-6.

55. Luong T.M., Le N.Q.K. Artificial intelligence in time-lapse system: advances, applications, and future perspectives in reproductive medicine. J Assist Reprod Genet. 2024;41(2):239–52. https://doi.org/10.1007/s10815-023-02973-y.

56. Petersen B.M., Boel M., Montag M., Gardner D.K. Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3. Hum Reprod. 2016;31(10):2231–44. https://doi.org/10.1093/humrep/dew188.

57. Reignier A., Girard J.M., Lammers J. et al. Performance of Day 5 KIDScore™ morphokinetic prediction models of implantation and live birth after single blastocyst transfer. J Assist Reprod Genet. 2019;36(11):2279–85. https://doi.org/10.1007/s10815-019-01567-x.

58. Tartia A.P., Wu C.Q., Gale J. et al. Time-lapse KIDScoreD5 for prediction of embryo pregnancy potential in fresh and vitrified-warmed single-embryo transfers. Reprod Biomed Online. 2022;45(1):46–53. https://doi.org/10.1016/j.rbmo.2022.03.019.

59. Brusilovsky I.A., Livshyts I.V. Morphological assessment of human embryos. «Colleagues, let’s make an arrangement!». [Morfologicheskaya ocenka embrionov cheloveka. «Kollegi, davajte dogovorimsya!»]. Problemy reprodukcii. 2018;24(2):63–8. (In Russ.). https://doi.org/10.17116/repro201824263-68.

60. Lee C.I., Huang C.C., Lee T.H. et al. Associations between the artificial intelligence scoring system and live birth outcomes in preimplantation genetic testing for aneuploidy cycles. Reprod Biol Endocrinol. 2024;22(1):12. https://doi.org/10.1186/s12958-024-01185-y.

61. Ueno S., Berntsen J., Ito M. et al. Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: a single-center large cohort retrospective study. Fertil Steril. 2021;116(4):1172–80. https://doi.org/10.1016/j.fertnstert.2021.06.001.

62. Ezoe K., Shimazaki K., Miki T. et al. Association between a deep learning-based scoring system with morphokinetics and morphological alterations in human embryos. Reprod Biomed Online. 2022;45(6):1124–32. https://doi.org/10.1016/j.rbmo.2022.08.098.

63. Johansen M.N., Parner E.T., Kragh M.F. et al. Comparing performance between clinics of an embryo evaluation algorithm based on time-lapse images and machine learning. J Assist Reprod Genet. 2023;40(9):2129–37. https://doi.org/10.1007/s10815-023-02871-3.

64. Zhigalina D.I., Skryabin N.A., Kanbekova O.R. et al. Structure of chromosomal abnormalities in the cycles of IVF-PGS. [Struktura hromosomnyh anomalij v ciklah EKO-PGS]. Medicinskaya genetika. 2019;18(3):47–54 (In Russ.). https://doi.org/10.25557/2073-7998.2019.03.47-54.

65. Alfarawati S., Fragouli E., Colls P. et al. The relationship between blastocyst morphology, chromosomal abnormality, and embryo gender. Fertil Steril. 2011;95(2):520–4. https://doi.org/10.1016/j.fertnstert.2010.04.003.

66. Fragouli E. Next generation sequencing for preimplantation genetic testing for aneuploidy: friend or foe? Fertil Steril. 2018;109(4):606–7. https://doi.org/10.1016/j.fertnstert.2018.01.028.

67. Sato T., Sugiura-Ogasawara M., Ozawa F. et al. Preimplantation genetic testing for aneuploidy: a comparison of live birth rates in patients with recurrent pregnancy loss due to embryonic aneuploidy or recurrent implantation failure. Hum Reprod. 2019;34(12):2340–8. https://doi.org/10.1093/humrep/dez229.

68. Cozzolino M., Diaz-Gimeno P., Pellicer A., Garrido N. Evaluation of the endometrial receptivity assay and the preimplantation genetic test for aneuploidy in overcoming recurrent implantation failure. J Assist Reprod Genet. 2020;37(12):2989–97. https://doi.org/10.1007/s10815-020-01948-7.

69. Practice Committees of the American Society for Reproductive Medicine and the Society for Assisted Reproductive Technology. Electronic address: ASRM@asrm.org; Practice Committees of the American Society for Reproductive Medicine and the Society for Assisted Reproductive Technology. The use of preimplantation genetic testing for aneuploidy (PGT-A): a committee opinion. Fertil Steril. 2018;109(3):429–36. https://doi.org/10.1016/j.fertnstert.2018.01.002.

70. Fitz V.W., Kanakasabapathy M.K., Thirumalaraju P. et al. Should there be an "AI" in TEAM? Embryologists selection of high implantation potential embryos improves with the aid of an artificial intelligence algorithm. J Assist Reprod Genet. 2021;38(10):2663–70. https://doi.org/10.1007/s10815-021-02318-7.

71. Buldo-Licciardi J., Large M.J., McCulloh D.H. et al. Utilization of standardized preimplantation genetic testing for aneuploidy (PGT-A) via artificial intelligence (AI) technology is correlated with improved pregnancy outcomes in single thawed euploid embryo transfer (STEET) cycles. J Assist Reprod Genet. 2023;40(2):289–99. https://doi.org/10.1007/s10815-022-02695-7.

72. Chavez-Badiola A., Flores-Saiffe-Farías A., Mendizabal-Ruiz G. et al. 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.

73. Diakiw S.M., Hall J.M.M., VerMilyea M. et al. An artificial intelligence model correlated with morphological and genetic features of blastocyst quality improves ranking of viable embryos. Reprod Biomed Online. 2022;45(6):1105–17. https://doi.org/10.1016/j.rbmo.2022.07.018.

74. Popovic M., Borot L., Lorenzon A.R. et al. Implicit bias in diagnosing mosaicism amongst preimplantation genetic testing providers: results from a multicenter study of 36395 blastocysts. Hum Reprod. 2024;39(1):258–74. https://doi.org/10.1093/humrep/dead213.

75. Muñoz E., Bronet F., Lledo B. et al. Representing the Special Interest Group in Reproductive Genetics of the Spanish Society of Fertility. To transfer or not to transfer: the dilemma of mosaic embryos – a narrative review. Reprod Biomed Online. 2024;48(3):103664. https://doi.org/10.1016/j.rbmo.2023.103664.

76. Greco E., Minasi M.G., Fiorentino F. Healthy babies after intrauterine transfer of mosaic aneuploid blastocysts. N Engl J Med. 2015;373(21):2089–90. https://doi.org/10.1056/NEJMc1500421.

77. Greco E., Greco P.F., Listorti I. et al. The mosaic embryo: what it means for the doctor and the patient. Minerva Obstet Gynecol. 2024;76(1):89–101. https://doi.org/10.23736/S2724-606X.23.05281-8.

78. Yakovlev P., Vyatkina S., Polyakov A. et al. Neonatal and clinical outcomes after transfer of a mosaic embryo identified by preimplantation genetic testing for aneuploidies. Reprod Biomed Online. 2022;45(1):88–-100. https://doi.org/10.1016/j.rbmo.2022.01.010.

79. Basile N., Nogales Mdel C., Bronet F. et al. Increasing the probability of selecting chromosomally normal embryos by time-lapse morphokinetics analysis. Fertil Steril. 2014;101(3):699–704. https://doi.org/10.1016/j.fertnstert.2013.12.005.

80. Li X., Yao Y., Zhao D. et al. Clinical outcomes of single blastocyst transfer with machine learning guided noninvasive chromosome screening grading system in infertile patients. Reprod Biol Endocrinol. 2024;22(1):61. https://doi.org/10.1186/s12958-024-01231-9.

81. Ortiz J.A., Morales R., Lledó B. et al. Application of machine learning to predict aneuploidy and mosaicism in embryos from in vitro fertilization cycles. AJOG Glob Rep. 2022;2(4):100103. https://doi.org/10.1016/j.xagr.2022.100103.

82. Ma B.X., Zhao G.N., Yi Z.F. et al. Enhancing clinical utility: deep learning-based embryo scoring model for non-invasive aneuploidy prediction. Reprod Biol Endocrinol. 2024;22(1):58. https://doi.org/10.1186/s12958-024-01230-w.

83. Zou Y., Pan Y., Ge N. et al. Can the combination of time-lapse parameters and clinical features predict embryonic ploidy status or implantation? Reprod Biomed Online. 2022;45(4):643–51. https://doi.org/10.1016/j.rbmo.2022.06.007.

84. Yuan Z., Yuan M., Song X. et al. Development of an artificial intelligence based model for predicting the euploidy of blastocysts in PGT-A treatments. Sci Rep. 2023;13(1):2322. https://doi.org/10.1038/s41598-023-29319-z.

85. Popovic M., Dhaenens L., Boel A. et al. Chromosomal mosaicism in human blastocysts: the ultimate diagnostic dilemma. Hum Reprod Update. 2020;26(3):313–34. https://doi.org/10.1093/humupd/dmz050.

86. Diakiw S.M., Hall J.M.M., VerMilyea M.D. et al. Development of an artificial intelligence model for predicting the likelihood of human embryo euploidy based on blastocyst images from multiple imaging systems during IVF. Hum Reprod. 2022;37(8):1746–59. https://doi.org/10.1093/humrep/deac131.

87. Weimar C.H., Post Uiterweer E.D., Teklenburg G. et al. In-vitro model systems for the study of human embryo-endometrium interactions. Reprod Biomed Online. 2013;27(5):461–76. https://doi.org/10.1016/j.rbmo.2013.08.002.

88. Lacconi V., Massimiani M., Carriero I. et al. When the embryo meets the endometrium: identifying the features required for successful embryo implantation. Int J Mol Sci. 2024;25(5):2834. https://doi.org/10.3390/ijms25052834.

89. Ruiz-Alonso M., Valbuena D., Gomez C. et al. Endometrial Receptivity Analysis (ERA): data versus opinions. Hum Reprod Open. 2021;2021(2):hoab011. https://doi.org/10.1093/hropen/hoab011.

90. Li B., Duan H., Wang S. et al. Establishment of an artificial neural network model using immune-infiltration related factors for endometrial receptivity assessment. Vaccines. 2022;10(2):139. https://doi.org/10.3390/vaccines10020139.

91. Liang X., He J., He L. et al. An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study. Reprod Biomed Online. 2023;47(2):103204. https://doi.org/10.1016/j.rbmo.2023.03.015.

92. Sysoeva AP, Makarova NP, Kalinina EA, et al. Enhancing the efficiency of assisted reproductive technologies using artificial intelligence and machine learning at the embryological stage. [Povyshenie effektivnosti vspomogatel'nyh reproduktivnyh tekhnologij s pomoshch'yu iskusstvennogo intellekta i mashinnogo obucheniya na embriologicheskom etape]. Akusherstvo i ginekologiya. 2020;(7):28–36. (In Russ.). https://doi.org/10.18565/aig.2020.7.28-36.

93. Wald M., Sparks A., Sandlow J. et al. Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reprod Biomed Online. 2005;11(3):325–31. https://doi.org/10.1016/s1472-6483(10)60840-1.

94. Benchaib M., Labrune E., Giscard d'Estaing S. et al. Shallow artificial networks with morphokinetic time-lapse parameters coupled to ART data allow to predict live birth. Reprod Med Biol. 2022;21(1):e12486. https://doi.org/10.1002/rmb2.12486.

95. Kato K., Ueno S., Berntsen J. et al. Comparing prediction of ongoing pregnancy and live birth outcomes in patients with advanced and younger maternal age patients using KIDScore™ day 5: a large-cohort retrospective study with single vitrified-warmed blastocyst transfer. Reprod Biol Endocrinol. 2021;19(1):98. https://doi.org/10.1186/s12958-021-00767-4.

96. VerMilyea M., Hall J.M.M., Diakiw S.M. et al. Development of an artificial intelligence-based assessment model for prediction of embryo viability using static images captured by optical light microscopy during IVF. Hum Reprod. 2020;35(4):770–84. https://doi.org/10.1093/humrep/deaa013.

97. Savel'eva G.M., Konoplyannikov A.G., Gergert E.V. et al. Pregravid preparation for patients with a history of infertility and inefficacy of in vitro fertilization. [Pregravidarnaya podgotovka u bol'nyh s besplodiem i neeffektivnost'yu ekstrakorporal'nogo oplodotvoreniya v anamneze]. Rossijskij vestnik akushera-ginekologa. 2019;19(5):43–51. (In Russ.). https://doi.org/10.17116/rosakush20191905143.

98. Dobrokhotova Yu.E., Dzhokhadze L.S. Preconception care as a real way to improve the perinatal outcomes. [Kompleksnaya pregravidarnaya podgotovka – real'nyj put' uluchsheniya perinatal'nyh iskhodov]. Problemy reprodukcii. 2019;25(6):38–43. (In Russ.). https://doi.org/10.17116/repro20192506138.

99. Doskina E.V., Sarkisova A.A. Pregravid preparation and features of patients with endocrine pathologies. [Pregravidarnaya podgotovka i osobennosti pacientok s endokrinnymi patologiyami]. Spravochnik poliklinicheskogo vracha. 2018;(3):60–4. (In Russ.).

100. Shcherbakova L.N., Gavrikova P.A., Kupriyan A.A. et al. Reproductive potential regaining in patients with endometriosis-associated infertility. [Znachenie medikamentoznoj pregravidarnoj podgotovki v realizacii reproduktivnoj funkcii pri besplodii, obuslovlennom naruzhnym genital'nym endometriozom]. Klinicheskaya farmakologiya i terapiya. 2018;27(4):18–22. (In Russ.).

101. Kim H.K. The effects of artificial intelligence chatbots on women's health: a systematic review and meta-analysis. Healthcare (Basel). 2024;12(5):534. https://doi.org/10.3390/healthcare12050534.

102. Segundo E., Carrere-Molina J., Aragón M., Mallol-Parera R. Advancing geospatial preconception health research in primary care through medical informatics and artificial intelligence. Health Place. 2024;89:103337. https://doi.org/10.1016/j.healthplace.2024.103337.

103. Kaya Y., Bütün Z., Çelik Ö. et al. The early prediction of gestational diabetes mellitus by machine learning models. BMC Pregnancy Childbirth. 2024;24(1):574. https://doi.org/10.1186/s12884-024-06783-7.

104. Fraire-Zamora J.J., Ali Z.E., Makieva S. et al. #ESHREjc report: on the road to preconception and personalized counselling with machine learning models. Hum Reprod. 2022;37(8):1955–7. https://doi.org/10.1093/humrep/deac111.

105. Arora U., Sengupta D., Kumar M. et al. Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism. Placenta. 2023;140:109–16. https://doi.org/10.1016/j.placenta.2023.07.014.

106. Demkina E.A., Ivanova N.A. Legal and ethical aspects of the use of artificial intelligence in reproductive medicine. [Pravovye i eticheskie aspekty ispol'zovaniya iskusstvennogo intellekta v reproduktivnoj medicine]. Vestnik Saratovskoj gosudarstvennoj yuridicheskoj akademii. 2024;3(158):122–7. (In Russ.). https://doi.org/10.24412/2227-7315-2024-3-122-127.

107. Si K., Huang B., Jin L. Application of artificial intelligence in gametes and embryos selection. Hum Fertil. 2023;26(4):757–77. https://doi.org/10.1080/14647273.2023.2256980.

108. Hogan N.R., Davidge E.Q., Corabian G. On the ethics and practicalities of artificial intelligence, risk assessment, and race. J Am Acad Psychiatry Law. 2021;49(3):326–34. https://doi.org/10.29158/JAAPL.200116-20.

109. Serdarogullari M., Liperis G., Sharma K. et al. Unpacking the artificial intelligence toolbox for embryo ploidy prediction. Hum Reprod. 2023;38(12):2538–42. https://doi.org/10.1093/humrep/dead223.

110. Allahbadia G.N., Allahbadia S.G., Gupta A. In contemporary reproductive medicine human beings are not yet dispensable. J Obstet Gynaecol India. 2023;73(4):295–300. https://doi.org/10.1007/s13224-023-01747-x.

111. Senders J.T., Zaki M.M., Karhade A.V. et al. An introduction and overview of machine learning in neurosurgical care. Acta Neurochir. 2018;160(1):29–38. https://doi.org/10.1007/s00701-017-3385-8.

112. Horer S., Feichtinger M., Rosner M., Hengstschläger M. Pluripotent stem cell-derived in vitro gametogenesis and synthetic embryos – it is never too early for an ethical debate. Stem Cells Transl Med. 2023;12(9):569–75. https://doi.org/10.1093/stcltm/szad042.

113. Hengstschläger M. Artificial intelligence as a door opener for a new era of human reproduction. Hum Reprod Open. 2023;2023(4):hoad043. https://doi.org/10.1093/hropen/hoad043.

114. Harper J., Magli M.C., Lundin K. et al. When and how should new technology be introduced into the IVF laboratory? Hum Reprod. 2012;27(2):303–13. https://doi.org/10.1093/humrep/der414.

115. Medenica S., Zivanovic D., Batkoska L. et al. The future is coming: artificial intelligence in the treatment of infertility could improve assisted reproduction outcomes – the value of regulatory frameworks. Diagnostics. 2022;12(12):2979. https://doi.org/10.3390/diagnostics12122979.

116. Drapkina Yu.S., Kalinina E.A., Makarova N.P. et al. Artificial intelligence in reproductive medicine: ethical and clinical aspects. [Iskusstvennyj intellekt v reproduktivnoj medicine: eticheskie i klinicheskie aspekty]. Akusherstvo i ginekologiya. 2022;(11):37–44. (In Russ.). https://doi.org/10.18565/aig.2022.11.37-44.