Enhancing the efficiency of assisted reproductive technologies using artificial intelligence and machine learning at the embryological stage
The authors have carried out a systems analysis of the data available in the literature on the possibilities of using the latest artificial intelligence (AI) techniques in the field of assisted reproductive technologies (ART). The review covers a number of foreign and Russian publications on this topic. The analysis of the literature has led to the conclusion that scientific collaborations in the field of ART and AI open up new opportunities for working with the biological material of infertile patients and increase their chances of becoming parents. A more accurate and standardized analysis of the structure and morphology will enable clinical embryologists to select the most viable embryos for transfer and to use the best sperm for fertilization in ART programs. Despite the fact that many methods in this area still remain experimental and require further studies and improvement; these will be able to create the assisted systems implementing decision support. However, reproductive centers need the systems. The relevance of these systems in modern medicine leaves no doubt: tools are often lacking; the health-disease borders are quite wide and may overlap; the final prediction is subjective.Sysoeva A.P., Makarova N.P., Kalinina E.A., Skibina Yu.S., Zanishevskaya A.A., Yanchuk N.O., Gryaznov A.Yu.
Keywords
assisted reproductive technologies
artificial intelligence
embryos
sperm
pregnancy
in vitro fertilization
machine learning
References
- GBD 2017 Population and Fertility Collaborators. Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018; 392(10159): 1995-2051. https://dx.doi.org/10.1016/S0140-6736(18)32278-5.
- Mascarenhas M.N., Flaxman S.R., Boerma T., Vanderpoel S., Stevens G.A. National, regional, and global trends in infertility prevalence since 1990: a systematic analysis of 277 health surveys. PLoS Med. 2012; 9(12): e1001356. https://dx.doi.org/10.1371/journal.pmed.1001356.
- Turchi P. Prevalence, definition, and classification of infertility. In: Cavallini G., Beretta G. Clinical management of male infertility. Springer; 2015: 5-11.
- Wang J., Sauer M.V. In vitro fertilization (IVF): a review of 3 decades of clinical innovation and technological advancement. Ther. Clin. Risk Manag. 2006; 2(4): 355-64. https://dx.doi.org/10.2147/tcrm.2006.2.4.355.
- Gardner D.K., Sakkas D. Assessment of embryo viability: the ability to select a single embryo for transfer — a review. Placenta. 2003; 24(Suppl. B): S5-12. https://dx.doi.org/10.1016/s0143-4004(03)00136-x.
- Baxter A., Mayer J., Shipley S., Catherino W. Interobserver and intraobserver variation in day 3 embryo grading. Fertil. Steril. 2006; 86(6): 1608-15. https://dx.doi.org/10.1016/j.fertnstert.2006.05.037.
- Storr A., Venetis C. A., Cooke S., Kilani S., Ledger W. Inter-observer and intra-observer agreement between embryologists during selection of a single Day 5 embryo for transfer: a multicenter study. Hum. Reprod. 2017; 32(2): 307-14. https://dx.doi.org/10.1093/humrep/dew330.
- 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://dx.doi.org/10.1007/s10815-019-01408-x.
- Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 2001; 23(1): 89-109.
- Kaufmann J., Eastaugh J., Snowden S., Smye S., Sharma V. The application of neural networks in predicting the outcome of in-vitro fertilization. Hum. Reprod. 1997; 12(7): 1454-7. https://dx.doi.org/10.1093/humrep/12.7.1454.
- Badura A., Marzec-Wróblewska U., Kamiński P., Łakota P., Ludwikowski G., Szymański M., Karolina Wasilow K. Prediction of semen quality using artificial neural network. J. Appl. Biomed. 2019; 17(3): 167-74. https://dx.doi.org/10.32725/jab.2019.015.
- Ma J., Zhen A., Guan S., Liu C., Huang X. Predicting seminal quality using back-propagation neural networks with optimal feature subsets.In: International conference on brain inspired cognitive systems. Springer; 2018: 25-33.
- Gil D., Girela J.L., De Juan J., Gomez-Torres M.J., Johnsson M. Predicting seminal quality with artificial intelligence methods. Expert Syst. Appl. 2012; 39(16): 12564-73.
- Helwan A., Khashman A., Olaniyi E.O., Oyedotun O.K., Oyedotun O.A. Seminal quality evaluation with RBF neural network Bull. Transilvania Univ. Brasov. Mathematics, Informatics, Physics. Series III. 2016; 9(2): 137.
- Wald M., Sparks A., Sandlow J., Van-Voorhis B., Syrop C.H., Niederberger C.S. Computational models for prediction of IVF/ICSI outcomes with surgically retrieved spermatozoa. Reprod. Biomed. Online. 2005; 11(3): 325-31. https://dx.doi.org/ 10.1016/s1472-6483(10)60840-1.
- Mostaar A., Sattari M., Hosseini S., Deevband M.R. Use of artificial neural networks and PCA to predict results of infertility treatment in the ICSI method. J. Biomed. Phys. Eng. 2019; 9(6): 679-86. https://dx.doi.org/10.31661/jbpe.v0i0.1187.
- Linneberg C., Salamon P., Svarer C., Hansen L.K., Meyrowitsch J. Towards semen quality assessment using neural networks In: Proceedings of IEEE Workshop on Neural Networks for Signal Processing. 1994: 509-17.
- Mirsky S., Barnea I., Levi M., Greenspan H., Shaked N.T. Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning. Cytometry A. 2017; 91(9): 893-900. https://dx.doi.org/10.1002/cyto.a.23189.
- Alegre E., Biehl M., Petkov N., Sánchez L. Automatic classification of the acrosome status of boar spermatozoa using digital image processing and LVQ. Comput. Biol. Med. 2008; 38(4): 461-8.
- Bijar A., Benavent A.P., Mikaeili M., Khayati R. Fully automatic identification and discrimination of sperm’s parts in microscopic images of stained human semen smear. J. Biomed. Sci. Eng. 2012; 5(7): 384-95. https://dx.doi.org/10.4236/jbise.2012.57049.
- Tsai V., Zhuang B. MP46-10 An at-home system that adapts to different types of mobile phones for measuring sperm motility — verification of its performance of artificial intelligence (AI) sperm image recognition with cloud computing. J. Urol. 2019; 201(Suppl. 4). https://dx.doi.org/10.1097/01.JU.0000556302.57109.2b.
- Manna C., Nanni L., Lumini A., Pappalardo S. Artificial intelligence techniques for embryo and oocyte classification. Reprod. Biomed. Online. 2013; 26(1): 42-9.
- Rocha J., Passalia F., Matos F. et al. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Scientific Rep. 2017; 7(1): 1-10.
- VerMilyea M., Hall J.M.M., Diakiw S.M., Johnston A.., Nguyen T., Perugini D. 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://dx.doi.org/10.1093/humrep/deaa013.
- Kanakasabapathy M., Thirumalaraju P., Bormann C., Kandula H. et al. Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology. Lab. Chip. 2019; 19(24): 4139-45.
- Miyagi Y., Habara T., Hirata R. et al. Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image. Reprod. Med. Biol. 2019; 18(2): 204-11.
- Wong C., Loewke K.E., Bossert N., Behr B., De Jonge C.J., Baer T.M., Reijo Pera R.A. Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat. Biotechnol. 2010; 28(10): 1115.
- Conaghan J., Chen A., Willman S., Ivani K., Chenette P.E., 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.
Received 07.05.2020
Accepted 18.06.2020
About the Authors
Anastasia P. Sysoeva, embryologist of the Department of Assistive Technologies in the Treatment of Infertility, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov. E-mail: sysoeva.a.p@gmail.com. 4, Ac. Oparina str., Moscow, 117997, Russian Federation.Natalya P. Makarova, Doctor of Biological Sciences, Leading Researcher of the Department of Assistive Technologies in the Treatment of Infertility, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov. E-mail: np_makarova@oparina4.ru.
4, Ac. Oparina str., Moscow, 117997, Russian Federation.
Kalinina Elena Anatolievna, Doctor of Medical Sciences, Head of the Department of Assistive Technologies in the Treatment of Infertility, National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov. E-mail: e_kalinina@oparina4.ru.
4, Ac. Oparina str., Moscow, 117997, Russian Federation.
Skibina Julia Sergeevna, Candidate of Physics and Mathematics, Director of NPP Nanostructural Glass Technology LLC and ISTC Structural Nanobiophotonics
Address: E-mail: director@nano-glass.ru.
101, room III (PO Box 2985), pr. 50 years of October, Saratov, 410033, Russia.
Anastasia A. Zanishevskaya, Senior Researcher, NPP Nanostructural Glass Technology LLC, Head of the Advanced Research Department of the Structural Nanobiophotonics Research Center. E-mail: zanishevskayaaa@nano-glass.ru. 101, room III (PO Box 2985), pr. 50 years of October, Saratov, 410033, Russia.
Yanchuk Natalia Olegovna, Candidate of medical sciences, head of the sensor technology department of NPP Nanostructural Glass Technology LLC and deputy director of the Structural Nanobiophotonics Research Center. E-mail: info@nano-glass.ru.
101, room III (PO Box 2985), pr. 50 years of October, Saratov, 410033, Russia.
Aleksey Yu. Gryaznov, Leading Researcher, NPP Nanostructural Glass Technology LLC, Head of the Decision System Department of the Structural Nanobiophotonics Research Center. E-mail: info@nano-glass.ru. 101, room III (PO Box 2985), pr. 50 years of October, Saratov, 410033, Russia.
For citation: Sysoeva A.P., Makarova N.P., Kalinina E.A., Skibina Yu.S., Zanishevskaya A.A., Yanchuk N.O., Gryaznov A.Yu. Enhancing the efficiency of assisted reproductive technologies using artificial intelligence and machine learning at the embryological stage.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2020; 7: 28-36 (in Russian).
https://dx.doi.org/10.18565/aig.2020.7.28-36
Similar Articles
