PREGESTATIONAL NEURAL NETWORK PREDICTION OF FETAL GROWTH RESTRICTION OR SMALL-FOR-GESTATIONAL-AGE FETUS WITH SUBSEQUENT INTENSIVE CARE OF THE NEWBORN

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. © A group of authors, 2024.