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Title
Authors
No 8 (2025)
Artificial intelligence technologies in gynecology
Mozes V.G., Kotov R.M., Rudaeva E.V., Elgina S.I., Mozes K.B., Vavin G.V.
PDF
(Rus)
No 9 (2025)
Evaluation of embryonic ploidy
Yashchuk A.G., Gromenko D.D., Nasyrova S.F., Gromenko I.I.
PDF
(Rus)
No 4 (2025)
The use of artificial intelligence-based web application for managing health records
Tumanov N.A.
PDF
(Rus)
No 11 (2024)
Remote monitoring of pregnant women: the Orenburg experience
Savinova T.L., Grudkin A.A., Ermolaeva I.V., Kundik T.A., Zingerman B.V., Abu Al Laban N.A., Borodin R.A., Yakovleva I.V., Kholin A.M., Shuvalova M.P., Sukhikh G.T.
PDF
(Rus)
No 2 (2024)
Comparison of predictive models built with different machine learning techniques using the example of predicting the outcome of assisted reproductive technologies
Drapkina Y.S., Makarova N.P., Vasiliev R.A., Amelin V.V., Kalinina E.A.
PDF
(Rus)
No 12 (2025)
Development and validation of an artificial intelligence-based system for predicting preterm birth using clinical data
Boldina Y.S., Ivshin A.A., Svetova K.S.
PDF
(Rus)
No 10 (2024)
Pregestational neural network prediction of fetal growth restriction or small-for-gestational-age fetus with subsequent intensive care of the newborn
Ziyadinov A.A., Novikova V.A., Radzinsky V.E.
PDF
(Rus)
No 10 (2025)
Omics data analysis using deep learning-based framework in differential diagnosis of ovarian cancer
Iurova M.V., Tokareva A.O., Chagovets V.V., Starodubtseva N.L., Frankevich V.E.
PDF
(Rus)
No 7 (2024)
Assessment of the impact of male factor infertility on the outcomes of assisted reproductive technology programs using machine learning techniques
Drapkina Y.S., Makarova N.P., Kulakova E.V., Kalinina E.A.
PDF
(Rus)
No 3 (2024)
Application of various machine learning techniques to the analysis of clinical, anamnestic, and embryological data of patients undergoing assisted reproductive technologies
Drapkina Y.S., Makarova N.P., Vasiliev R.A., Amelin V.V., Frankevich V.E., Kalinina E.A.
PDF
(Rus)
No 10 (2023)
Development and validation of models to predict total and early-onset preeclampsia in the first trimester of pregnancy using machine learning algorithms
Andreychenko A.E., Luchinin A.S., Ivshin A.A., Ermak A.D., Novitskiy R.E., Gusev A.V.
PDF
(Rus)
No 6 (2025)
The possibilities of using machine learning and artificial intelligence methods for morphological analysis of the placenta
Tumanova U.N., Tumanov N.A., Shchegolev A.I., Serov V.N.
PDF
(Rus)
1 - 12 of 12 Items

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