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Congenital malformations of infectious origin
Shipitsyna E.V., Bespalova O.N., Talantova O.E., Kogan I.Y.
Rationale for using clinical indicators to stratify pregnant women according to risk of Preeclampsia severity
Tezikov Y.V., Lipatov I.S., Khalitova A.I., Tyutyunnik V.L., Kan N.E., Yurasova E.V., Rudneva A.A., Garazhankin A.D.
Magnetic resonance imaging in the planning and monitoring of the treatment of cervical cancer
Solopova A.E., Bendzhenova B.B.
Clinical and anamnestic factors in the prediction and diagnosis of fetal growth restriction
Volochaeva M.V., Kan N.E., Tyutyunnik V.L., Amiraslanov E.Y., Leonova A.A., Soldatova E.E.
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.
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.
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.
Prediction of neonatal complications based on quantitative proteome analysisin blood of pregnant women with fetal growth restriction
Volochaeva M.V., Tokareva A.O., Bugrova A.E., Brzhozovskiy A.G., Kukaev E.N., Tyutyunnik V.L., Kan N.E., Starodubtseva N.L.
Association between new coronavirus infection and fetal growth restriction
Lipatov I.S., Tezikov Y.V., Kalinkina O.B., Tyutyunnik V.L., Kan N.E., Majorova M.O., Yakovleva M.A.
Using machine learning to analyze the lipid profile of culture medium and predict the efficacy of assisted reproductive technologies
Drapkina Y.S., Makarova N.P., Chagovets V.V., Vasiliev R.A., Amelin V.V., Kalinina E.A.
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