Application of fiber optic methods and artificial intelligence in ejaculate diagnosis in infertile males in the assisted reproductive technology programs

Gennady T. Sukhikh; Сухих Геннадий Тихонович; Julia S. Skibina; Скибина Юлия Сергеевна; Anastasia A. Zanishevskaya; Занишевская Анастасия Андреевна; Andrey A. Shuvalov; Шувалов Андрей Александрович; Natalia O. Yanchuk; Янчук Наталья Олеговна; Aleksey Yu. Gryaznov; Грязнов Алексей Юрьевич; Anastasia P. Sysoeva; Сысоева Анастасия Павловна; Nataliya P. Makarova; Макарова Наталья Петровна; Elvira Z. Valiakhmetova; Валиахметова Эльвира Зилявировна; Elena A. Kalinina; Калинина Елена Анатольевна; Pavel A. Lepilin; Лепилин Павел Андреевич

doi:10.18565/aig.2021.7.74-80

Application of fiber optic methods and artificial intelligence in ejaculate diagnosis in infertile males in the assisted reproductive technology programs

Authors: Sukhikh G.T.¹, Skibina J.S.², Zanishevskaya A.A.², Shuvalov A.A.², Yanchuk N.O.², Gryaznov A.Y.², Sysoeva A.P.¹, Makarova N.P.¹, Valiakhmetova E.Z.¹, Kalinina E.A.¹, Lepilin P.A.²
Affiliations:
1. Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of the Russian Federation
2. "Nanostructured Glass Technology" Research Production Enterprise, "Structure-Mediated Nanobiophotonics" International Research and Education Center
Issue: No 7 (2021)
Pages: 74-80
Section: Articles
URL: https://journals.eco-vector.com/0300-9092/article/view/249353
DOI: https://doi.org/10.18565/aig.2021.7.74-80
ID: 249353

Cite item

Full Text

Open Access
Restricted Access

Access granted
Restricted Access

Subscription or Fee Access

Abstract
Full Text
About the authors
References
Supplementary files
Statistics

Abstract

Objective. To establish regularities in the transmission spectrum of microstructural waveguides filled with isolated spermatozoa and seminal plasma from males with various disorders of spermatogenesis and, on this basis, to build a neural network that analyzes the spectral characteristics of sperm. Materials and methods. The spectral characteristics of345isolated spermatozoal samples and 209seminal plasma samples were analyzed using photonic crystal waveguides as optical sensors, and an artificial neural network, the prediction accuracy of which was 100%, was built. The patients, whose ejaculate was included in the study, were also surveyed. The main parameters in the questionnaire concerned the factors influencing spermatogenesis. Results. A neural network based on a multilayerperceptron was created, which proved to be effective in analyzing the spectral characteristics of seminal plasma and a sperm fraction. The created neural network of the ejaculate makes it possible to determine the “norm” and “pathology” with the highest accuracy. The use of artificial intelligence to analyze survey results on the male lifestyle proved to be less effective. The rate of correct answers was 88% for the test set of characteristics and 84% for the control set. Nevertheless, the developed neural network can be used for preliminary assessment and prediction of the sperm prof ile based on the results of a patient’s survey. Conclusion. Fiber optic methods in the diagnosis of the ejaculate are perspective and promising. There remains an unresolved issue regarding the absence of both natural and in vitro fertilization pregnancies in the presence of normal sperm counts. The introduction of new tests for seminal plasma and spermatozoa is an urgent task of reproductive medicine; it is especially interesting to use the capabilities of artificial intelligence to assess the fertility of both men and women.

Keywords

assisted reproductive technologies, in vitro fertilization, ejaculate estimation, male factor infertility, microstructural waveguide, neural network, spectroscopy, infertility

Full Text

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.
Rumbold A.R., Sevoyan A., Oswald T.K., Fernandez R.C., Davies M.J., Moore V.M. Impact of male factor infertility on offspring health and development. Fertil. Steril. 2019; 111(6): 1047-53. https://dx.doi.org/10.1016/j.fertnstert.2019.05.006.
WHO, UNEP. State of the science of endocrine disrupting chemicals 2012: an assessment of the state of the science of endocrine disruptors prepared by a group of experts for the United Nations Environment Program and World Health Organization. Geneva: WHO; 2013.
Huang C., Li B., Xu K., Liu D., Hu J., Yang Y. et al. Decline in semen quality among 30,636 young Chinese men from 2001 to 2015. Fertil. Steril. 2017; 107(1): 83-8. e2. https://dx.doi.org/10.1016/j.fertnstert.2016.09.035.
Ефремов Е.А., Касатонова Е.В., Мельник Я.И., Никушина АА. Почему не обновляются рекомендации по исследованию эякулята? Урология. 2019; 4: 148-54. [Efremov E.A., Kasatonova E.V., Melnik Ya.I., Nikushina A.A. Why are recommendations on the study of ejaculate not updated? Urology. 2019: 4: 148-54. (in Russian)]. https://dx.doi.org/10.18565/urology.2019A148-154.
Yibre A.M., Koqer B. Semen quality predictive model using Feed Forwarded Neural Networktrained by Learning-Based Artificial Algae Algorithm. Engineering Science and Technology: An International Journal (JESTECH). 2021; 24(2): 310-8. https://dx.doi.org/10.1016/j.jestch.2020.09.001.
Тучин В.В. Лазеры и волоконная оптика в биомедицинских исследованиях. 2-е изд. М.: ФИЗМАТЛИТ; 2010. 488 с. [Tuchin V.V. Lasers and fiber optics in biomedical research. 2nd ed., correct and additional. M.: FIZMATLIT; 2010. 488 p. (in Russian)]. ISBN 978-5-9221-1278-9.
Badura A., Marzec-Wroblewska U., Kaminski P., Lakota P., Ludwikowski G., Szymanski M., Bucinski A. Prediction of semen quality using artificial neural network. J. Appl. Biomed. 2019; 17(5): 167-74. https://dx.doi.org/10.32725/jab.2019.015.
Ma J., Zhen A., Guan S.U., Liu C., Huang X. Predicting seminal quality using back-propagation neural networks with optimal feature subsets. In: Advances in Brain Inspired Cognitive Systems: 9th International Conference, BICS 2018. Xi'an, China, July 7-8, 2018, Proceedings. 2018: 25-33. https://dx.doi.org/10.1007/978-3-030-00563-4_3.
Малинин А.В., Скибина Ю.С., Тучин В.В., Чайников М.В., Белоглазов В.И., Силохин И.Ю., Занишевская А.А., Дубровский В.А., Долмашкин А.А. Применение фотонно-кристаллических волноводов с полой сердцевиной в качестве биологических сенсоров. Квантовая электроника. 2011; 41(4): 302-7.
Zanishevskaya A.A., Malinin A.V., Tuchin V.V., Skibina Y.S., Silokhin I.Y. Photonic crystal waveguide biosensor. J. Innov. Opt. Health Sci. 2013; 6(2): 1350008. https://dx.doi.org/10.1142/S1793545813500089.
Мюллер А., Гвидо С. Введение в машинное обучение с помощью Python. Руководство для специалистов по работе с данными. СПб.: Альфа-книга; 2017.
Грибачев В. Настоящее и будущее нейронных сетей. Компоненты и технологии. 2006; 5: 146-50.
Krizhevsky A., Sutskever I., Hinton G.E. Imagenet classification with deep convolutional neural networks. In: Bartlett P.L., Pereira F.C.N., Burges C.J.C., Bottou L., Weinberger K.Q., eds. Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held, Lake Tahoe, Nevada, United States, 2012, 3-6 December. 2012: 1106-14.

Supplementary files

Supplementary Files

Action

1. JATS XML

Download

Username
Password
Remember me

Forgot password?	Register

Username
Password
Remember me

Forgot password?	Register

Application of fiber optic methods and artificial intelligence in ejaculate diagnosis in infertile males in the assisted reproductive technology programs

Full Text

Abstract

Keywords

Full Text

About the authors

References

Supplementary files

This website uses cookies