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.
Objective: To create and test a prototype of a neural network tool for pregestational stratification of high-risk women according to the delivery of a fetus with growth restriction (FGR) or small for gestational age (SGA) and the need for intensive care of the newborn.
Materials and methods: This was a prospective cohort study which was conducted at the Perinatal Centre of the Republic of Crimea, N.A. Semashko Republican Clinical Hospital from 2018 to 2020. The study included 611 women with singleton pregnancies complicated by FGR (n=435) and SGA (n=176). The prognosis was performed using a personal computer: Statistica 12.0 software, Automated Neural Networks module.
Results: The use of automated neural network model analysis provided prototype tools for pregestational stratification of women at risk for FGR or SGA (model 1); the need for neonatal intensive care (model 2), including respiratory support (model 3). This neural network prediction effectively (accuracy of training, testing and validation of neural networks up to 100%) provides maternal clinical and anamnestic and socio-demographic parameters (place and permanence of residence, education, occupation, marital status; age, including paternal), height-weight, characteristics of reproductive function, reproductive experience, fetal weight in previous deliveries, gravidity, pre-eclampsia in previous pregnancy, the history of previous delivery and its mode).
Conclusion: The obtained neural network models demonstrate the possibility of developing tools that provide predictive clinical and management analytics. The obtained neural network models demonstrate the possibility of developing tools that provide predictive clinical and management analytics. These tools can be used by clinicians in daily practice and help them choose optimal pregnancy management, screening and diagnosis of disorders, and timely routing of pregnant women at the institutions of the appropriate level.
Authors’ contributions: Ziyadinov A.A. – developing the concept and design of the study, data analysis, writing and editing the text, approval of the manuscript for publication; Novikova V.A. – developing the concept and design of the study, statistical processing of the data and interpretation of the results; Radzinsky V.E. – developing the concept and design of the study, editing the text, approval of the manuscript for publication.
Conflicts of interest: Authors declare lack of the possible conflicts of interest.
Funding: The study was conducted without sponsorship.
Ethical Approval: The study was approved by the Ethical Review Board of the V.I. Vernadsky Crimean Federal University.
Patient Consent for Publication: The patients provided an informed consent for the publication of their data.
Authors' Data Sharing Statement: The data supporting the findings of this study are available on request from the corresponding author after approval from the principal investigator.
For citation: Ziyadinov A.A., Novikova V.A., Radzinsky V.E. Pregestational neural network prediction of fetal growth restriction or small-for-gestational-age fetus with subsequent intensive care of the newborn.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2024; (10): 60-73 (in Russian)
https://dx.doi.org/10.18565/aig.2024.124
Keywords
pregnancy
fetal growth restriction
small for gestational age
artificial intelligence
neural network prediction
predictive clinical analytics
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Received 22.05.2024
Accepted 22.10.2024
About the Authors
Arsen A. Ziyadinov, PhD, Associate Professor at the Department of Obstetrics, Gynecology and Perinatology No. 1 of S.I. Georgievsky Medical Institute, V.I. Vernadsky Crimean Federal University; Obstetrician-Gynecologist at the Perinatal Center of N.A. Semashko Republican Clinical Hospital, 295017, Russia, Republic of Crimea, Simferopol, Semashko str., 8, ars-en@yandex.ruVladislava A. Novikova, Dr. Med. Sci., Professor of the Department of Obstetrics and Gynecology with the course of Perinatology, Medical Institute of Peoples’ Friendship University of Russia, 117198, Russia, Moscow, Miklukho-Maklaya str., 6, kafedra-aig@mail.ru
Victor E. Radzinsky, Dr. Med. Sci., Professor, Corresponding Member of the RAS, Head of the Department of Obstetrics and Gynecology with the course of Perinatology, Medical Institute of Peoples’ Friendship University of Russia, 117198, Russia, Moscow, Miklukho-Maklaya str., 6, kafedra-aig@mail.ru
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