Search for the predictors of fetal growth restriction: from a measuring tape to artificial intellect

Fetal growth restriction (FGR) is a common obstetric pathology, the frequency of which in various populations may amount to as much as 5-15%. This pregnancy complication is associated with high perinatal morbidity and mortality rates, leading to serious complications for the fetus, newborn, and child. The literature review presents a history of searching for FGR predictors from a measuring tape to artificial intellect. It discusses the importance of external fetometry, including clinical practice guidelines and Cochrane Reviews. There are data on the significance of ultrasonic fetometry. The review elucidates the limited role of some biomarkers in the first trimester screening program for the prediction and diagnosis of FGR. It analyzes a large number of risk factors and their heterogeneities that hinder the use of generally accepted statistical methods. There is a greater interest in the use of machine learning and artificial intelligence, including that in obstetrics and perinatology. Particular attention is given to the analysis and discussion of proposed models and algorithms for the prediction of FGR in recent years. Conclusion: The dawning age of machine learning and artificial intelligence allows the prediction and timely diagnosis of FGR. Early prediction will facilitate personalized clinical monitoring and management, which will be able to improve fetal and newborn health.

fetal growth restriction, external obstetric examination, ultrasonic fetometry, biomarkers, risk factors, prediction, machine learning, artificial intelligence, prediction, perinatal health