Artificial intelligence in pregnancy prediction

Buyanova S.N.; Schukina N.A.; Temlyakov A.Yu.; Glebov T.A.

doi:https://doi.org/10.17116/rosakush20232302183

Buyanova S.N.

Moscow Regional Research Institute of Obstetrics and Gynecology

ORCID: 0000-0002-1358-6640

Schukina N.A.

Moscow Regional Research Institute of Obstetrics and Gynecology;
M.F. Vladimirskiy Moscow Regional Research Institute

Temlyakov A.Yu.

LLC «DiaLab Plus»

ORCID: 0000-0002-0624-2137

Glebov T.A.

Moscow Regional Research Institute of Obstetrics and Gynecology

ORCID: 0000-0001-6816-5360

Artificial intelligence in pregnancy prediction

Authors:

Buyanova S.N., Schukina N.A., Temlyakov A.Yu., Glebov T.A.

More about the authors

Journal: Russian Bulletin of Obstetrician-Gynecologist. 2023;23(2): 83‑87

DOI: 10.17116/rosakush20232302183

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To cite this article:

Buyanova SN, Schukina NA, Temlyakov AYu, Glebov TA. Artificial intelligence in pregnancy prediction. Russian Bulletin of Obstetrician-Gynecologist. 2023;23(2):83‑87. (In Russ.)
https://doi.org/10.17116/rosakush20232302183

Подписка 2026 Российский вестник акушера-гинеколога (Russian Bulletin of Obstetrician-Gynecologist)

Использование инфографики авторами научных работ

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OBJECTIVE

To study the power of artificial intelligence in predicting the onset of pregnancy by evaluating patients’ hormonal status and medical history.

MATERIAL AND METHODS

A retrospective analysis was made of 334 case histories of patients aged 20 to 48 years who underwent in vitro fertilization (excluding cryoprocedures) between July 2011 and January 2019 using the SPERO-screen software algorithm.

RESULTS

The probability of pregnancy on dates significantly different from the software-calculated dates is significantly lower than the data reported in the world literature. Use of the data analysis algorithm created on the basis of artificial intelligence makes it possible to increase the probability of pregnancy by more than 2-fold.

CONCLUSION

On the basis of the analysis of a woman’s hormonal status and anamnesis data with the application of machine learning technologies with the application of artificial neural networks, prepared on the basis of thousands of clinical cases of successful pregnancy, it is possible to predict the most favorable menstrual cycles and specific dates of conception.

Keywords:

artificial intelligence

machine learning

reproductology

infertility

IVF

SPERO-screen

Authors:

Buyanova S.N.

Moscow Regional Research Institute of Obstetrics and Gynecology

ORCID: 0000-0002-1358-6640

Schukina N.A.

Moscow Regional Research Institute of Obstetrics and Gynecology;
M.F. Vladimirskiy Moscow Regional Research Institute

Temlyakov A.Yu.

LLC «DiaLab Plus»

ORCID: 0000-0002-0624-2137

Glebov T.A.

Moscow Regional Research Institute of Obstetrics and Gynecology

ORCID: 0000-0001-6816-5360

Received:

30.01.2023

Accepted:

06.02.2023

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