NEW TECHNOLOGIES FOR THE DIAGNOSIS OF HYPOXIC CONDITIONS IN THE TERM NEWBORN

A meta-analysis of the recent years indicates an increase rate of morbidity in pregnant women, leading to the development of hypoxic conditions in the fetus. It often leads to future brain lesions. Lesions of the central nervous system in children place a heavy social burden on the state and society. Therefore, the search for solutions to the problems of early disease detection does not lose its relevance throughout the entire world.

The aim of the study: To develop a technology for early diagnosis of hypoxic conditions in full-term newborns. Material and method: The study included 105 full-term newborns, which were divided into 3 groups: group 1 - full-term newborns born in moderate asphyxia, group 2 - full-term newborns who experienced chronic fetal hypoxia and were born with a normal Apgar score, group 3 - full-term newborns who did not experience chronic fetal hypoxia and were born with a normal Apgar score. Statistical analysis was carried out using the IBM SPSS Statistics (Version 25.0) software package. Statistically significant parameters were included in the database, which formed the basis for training the multilayer perceptron.

Results: The structure of the trained neural network included 12 input neurons, reflecting the level of significant anamnestic risk factors for pregnancy (threatened miscarriage, preeclampsia, placental insufficiency) and immunological parameters (interleukin (IL)-1β, IL-4, IL-6, IL-8, tumor necrosis factor (TNF)-α, neuron-specific enolase (NSE) in cord blood). The resulting neural network has high sensitivity and specificity for all the studied groups, the accuracy of predictions was 96.2%.

Conclusions: The combination of parameters of anamnestic risk factors for the course of pregnancy and data on the concentrations of IL-1β, IL-4, IL-6, IL-8, TNF-α, NSE in cord blood, included in the structure of the neural network, makes it possible to diagnose the development of hypoxic conditions with a high degree of probability in term newborns. The use of this technology in practical healthcare will allow early detection of the pathology of the central nervous system.

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