Андрология и генитальная хирургия. 2022; 23: 15-25
Искусственный интеллект в репродуктивной медицине
https://doi.org/10.17650/2070-9781-2022-23-4-15-25Аннотация
В настоящее время стремительно развивающиеся компьютерные и цифровые технологии входят в различные сферы жизни. Их бурное развитие и широкое применение стимулировали разработку и совершенствование систем искусственного интеллекта, которые позволяют решать сложные задачи в различных областях, в том числе научных, технологических, медицинских и других.
В статье рассматриваются терминология и принципы систем искусственного интеллекта, а также современные возможности и перспективы использования технологий, созданных на их основе, направления их применения в репродуктивной медицине для решения различных научных проблем и практических задач. Они могут быть использованы в диагностике и оценке риска развития различных болезней и осложнений, генетическом тестировании и оценке его результатов, прогнозировании наступления беременности и оценке фертильности, анализе половых клеток, а также для выбора наиболее качественных эмбрионов, полученных в программах экстракорпорального оплодотворения, и решения других задач.
Список литературы
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24. Dimitriadis I., Bormann C.L., Kanakasabapathy M.K. et al. Automated smartphone-based system for measuring sperm viability, DNA fragmentation, and hyaluronic binding assay score. PLoS One 2019;14(3):e0212562. DOI: 10.1371/journal.pone.0212562
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26. Patel D.P., Gross K.X., Hotaling J.M. Can artificial intelligence drive optimal sperm selection for in vitro fertilization? Fertil Steril 2021;115(4):883. DOI: 10.1016/j.fertnstert.2021.02.004
27. You J.B., McCallum C., Wang Y. et al. Machine learning for sperm selection. Nat Rev Urol 2021;18(7):387–403. DOI: 10.1038/s41585-021-00465-1
28. Dimitriadis I., Zaninovic N., Badiola A.C., Bormann C.L. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online 2022;44(3):435–48. DOI: 10.1016/j.rbmo.2021.11.003
29. Zaninovic N., Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril 2020;114(5):914–20. DOI: 10.1016/j.fertnstert.2020.09.157
30. Riegler M.A., Stensen M.H., Witczak O. et al. Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum Reprod 2021;36(9):2429–42. DOI: 10.1093/humrep/deab168
31. Kragh M.F., Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet 2021;38(7):1675–89. DOI: 10.1007/s10815-021-02254-6
32. Loewke K., Cho J.H., Brumar C.D. et al. Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos. Fertil Steril 2022;117(3):528–35. DOI: 10.1016/j.fertnstert.2021.11.022
33. Armstrong S., Bhide P., Jordan V. et al. Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst Rev 2018;5(5):CD011320. DOI: 10.1002/14651858.CD011320.pub3
Andrology and Genital Surgery. 2022; 23: 15-25
Artificial intelligence in reproductive medicine
https://doi.org/10.17650/2070-9781-2022-23-4-15-25Abstract
Currently, rapidly developing computer and digital technologies are widely included in variousspheres of life. Their rapid development and widespread using have stimulated the development and improvement of artificial intelligence systems that allow solving various complex tasks, applying them in variousfields, including scientific,technological, medical and others. The article discussesthe terminology and principles of artificialintelligence systems, aswell asmodern opportunities and prospects for the use of technologies created on their basis, the directions of their application in reproductive medicine to solve various scientific problems and practical tasks. It can be used in the diagnosis and assessment of the risk of development of various diseases and complications, genetic testing and evaluation ofitsresults, prediction of pregnancy and fertility assessment, analysis of germ cells, also asselection ofthe highest quality embryosin in vitro fertilization programs and in solving othertasks.
References
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11. Curchoe C.L., Flores-Saiffe Farias A., Mendizabal-Ruiz G., Chavez-Badiola A. Evaluating predictive models in reproductive medicine. Fertil Steril 2020;114(5):921–6. DOI: 10.1016/j.fertnstert.2020.09.159
12. Curchoe C.L., Malmsten J., Bormann C. et al. Predictive modeling in reproductive medicine: where will the future of artificial intelligence research take us? Fertil Steril 2020;114(5):934–40. DOI: 10.1016/j.fertnstert.2020.10.040
13. Hajirasouliha I., Elemento O. Precision medicine and artificial intelligence: overview and relevance to reproductive medicine. Fertil Steril 2020;114(5):908–13. DOI: 10.1016/j.fertnstert.2020.09.156
14. Fernandez E.I., Ferreira A.S., Cecílio M.H.M. et al. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J Assist Reprod Genet 2020;37(10):2359–76. DOI: 10.1007/s10815-020-01881-9
15. Zhang Y., Shen L., Yin X., Chen W. Live-birth prediction of natural-cycle in vitro fertilization using 57,558 linked cycle records: a machine learning perspective. Front Endocrinol (Lausanne) 2022;13:838087. DOI: 10.3389/fendo.2022.838087
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19. Jhee J.H., Lee S., Park Y. et al. Prediction model development of late-onset preeclampsia using machine learning-based methods. PLoS One 2019;14(8):e0221202. DOI: 10.1371/journal.pone.0221202
20. Bruno V., D’Orazio M., Ticconi C. et al. Machine Learning (ML) based-method applied in recurrent pregnancy loss (RPL) patients diagnostic work-up: a potential innovation in common clinical practice. Sci Rep 2020;10(1):7970. DOI: 10.1038/s41598-020-64512-4
21. WHO laboratory manual for the examination and processing of human semen. 6th edn. Geneva, 2021. Available at: https://apps.who.int/iris/rest/bitstreams/1358672/retrieve
22. Kanakasabapathy M.K., Sadasivam M., Singh A. et al. An automated smartphone-based diagnostic assay for point-of-care semen analysis. Sci Transl Med 2017;9(382):eaai7863. DOI: 10.1126/scitranslmed.aai7863
23. Agarwal A., Panner Selvam M.K., Sharma R. et al. Home sperm testing device versus laboratory sperm quality analyzer: comparison of motile sperm concentration. Fertil Steril 2018;110(7):1277–84. DOI: 10.1016/j.fertnstert.2018.08.049
24. Dimitriadis I., Bormann C.L., Kanakasabapathy M.K. et al. Automated smartphone-based system for measuring sperm viability, DNA fragmentation, and hyaluronic binding assay score. PLoS One 2019;14(3):e0212562. DOI: 10.1371/journal.pone.0212562
25. Mirsky S.K., Barnea I., Levi M. et al. Automated analysis of individual sperm cells using stain-free interferometric phase microscopy and machine learning. Cytometry A 2017;91(9):893–900. DOI: 10.1002/cyto.a.23189
26. Patel D.P., Gross K.X., Hotaling J.M. Can artificial intelligence drive optimal sperm selection for in vitro fertilization? Fertil Steril 2021;115(4):883. DOI: 10.1016/j.fertnstert.2021.02.004
27. You J.B., McCallum C., Wang Y. et al. Machine learning for sperm selection. Nat Rev Urol 2021;18(7):387–403. DOI: 10.1038/s41585-021-00465-1
28. Dimitriadis I., Zaninovic N., Badiola A.C., Bormann C.L. Artificial intelligence in the embryology laboratory: a review. Reprod Biomed Online 2022;44(3):435–48. DOI: 10.1016/j.rbmo.2021.11.003
29. Zaninovic N., Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril 2020;114(5):914–20. DOI: 10.1016/j.fertnstert.2020.09.157
30. Riegler M.A., Stensen M.H., Witczak O. et al. Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum Reprod 2021;36(9):2429–42. DOI: 10.1093/humrep/deab168
31. Kragh M.F., Karstoft H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J Assist Reprod Genet 2021;38(7):1675–89. DOI: 10.1007/s10815-021-02254-6
32. Loewke K., Cho J.H., Brumar C.D. et al. Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos. Fertil Steril 2022;117(3):528–35. DOI: 10.1016/j.fertnstert.2021.11.022
33. Armstrong S., Bhide P., Jordan V. et al. Time-lapse systems for embryo incubation and assessment in assisted reproduction. Cochrane Database Syst Rev 2018;5(5):CD011320. DOI: 10.1002/14651858.CD011320.pub3
