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

Gumeniuk Е.G., Ivshin A.A., Boldina Yu.S.

Department of Obstetrics and Gynecology, Dermatovenereology, Medical Institute, Petrozavodsk State University, Petrozavodsk, Russia
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
risk factors
machine learning
artificial intelligence
perinatal health


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Received 08.08.2022

Accepted 11.10.2022

About the Authors

Elena G. Gumeniuk, Dr. Med. Sci, Professor, Professor of the Department of Obstetrics and Gynecology, Dermatovenerology of the Medical Institute, Petrozavodsk State University; Chairman of the Karelian Association of Obstetricians and Gynecologists, +7(909)567-12-51,,
31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia.
Alexander A. Ivshin, PhD, Associate Professor, Head of the Department of Obstetrics and Gynecology, Dermatovenerology of the Medical Institute, Petrozavodsk State University, +7(909)567-12-51,, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia.
Yuliya S. Boldina, Assistant, Department of Obstetrics and Gynecology and Dermatovenerology of the Medical Institute, Graduate Student, Petrozavodsk State University,
+7(981)405-85-24,, 31, Krasnoarmeyskaya str., Petrozavodsk, Republic of Karelia, 185035, Russia.
Corresponding author: Alexander A. Ivshin,

Authors’ contributions: Gumeniuk Е.G. – design, search, analysis, and translation of literature, writing and editing the literature review; Ivshin A.A. – idea, participation in the writing and editing the scientific review; Boldina Yu.S. – participation in the writing and editing the scientific review.
Conflicts of interest: The authors declare that there are no conflicts of interest.
Funding: The investigation has been financially supported by the Ministry of Science and Higher Education of the Russian Federation under Agreement No. 075-15-2021-665; the investigation has been conducted using a unique scientific attitude (USA) “Multicomponent software and hardware complex for automated collection, storage, labeling of research and clinical biomedical data, their unification and analysis based on the data center, by applying artificial intelligence technologies” (Registration Number 2075518).
For citation: Gumeniuk Е.G., Ivshin A.A., Boldina Yu.S.
Search for the predictors of fetal growth restriction:
from a measuring tape to artificial intellect.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2022; 12: 18-24 (in Russian)

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