Prediction of fetal growth restriction using machine learning algorithms

DOI

https://dx.doi.org/10.18565/aig.2025.135

Kan N.E., Leonova A.A., Tyutyunnik V.L., Soldatova E.E., Ryzhova K.O., Serebriakova A.P.
1) Academician V.I. Kulakov National Medical Research Centre of Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, Moscow, Russia; 2) Primorsky Krai Perinatal Center, Vladivostok, Russia

Objective: To investigate the significant clinical and anamnestic predictors of fetal growth restriction (FGR) and develop effective predictive models using machine learning methods (MLM).
Materials and methods: This retrospective study included 620 pregnant women who were observed and delivered at the V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia. The study group comprised 300 patients with FGR, while the control group included 320 patients with healthy pregnancies. An analysis of the clinical and anamnestic data was conducted to build MLM models, including logistic regression and random forest.
Results: The logistic regression model identified the following predictors: age over 40 years, height less than 1.60 m, chronic arterial hypertension, smoking, a history of FGR, and threatened miscarriage in the first trimester with the formation of retrochorial hematoma and bleeding. This model predicts the development of FGR with a sensitivity of 73% and specificity of 80% (AUC 0.81). An alternative model constructed using random forest demonstrated an increased sensitivity of 78% and a decreased specificity of 74% (AUC 0.79). Within the random forest framework, the most significant contributors to the accuracy of the prognosis were age over 40 years, height less than 1.60 m, chronic arterial hypertension, a history of surgery resulting in a uterine scar, a history of FGR, and threatened miscarriage in the first trimester with retrochorial hematoma without bleeding.
Conclusion: Both models exhibited high predictive value for screening for FGR. Logistic regression offers interpretability, whereas random forest enhances the accuracy by accounting for nonlinear relationships. Implementing these models in clinical practice will optimize the monitoring of pregnant women at risk.

Authors’ contributions: Kan N.E., Leonova A.A., Tyutyunnik V.L., Soldatova E.E., Ryzhova K.O., Serebriakova A.P. – conception and design of the study, obtaining data for analysis, review of publications, processing and analysis of material on the topic, drafting of the manuscript, editing of the manuscript.
Conflicts of interest: The authors have no conflicts of interest to declare.
Funding: The study was conducted within the framework of the initiative project «Epigenetic Criteria for Diagnosing Fetal Growth Delay from the Perspective of Neurogenesis Dysfunction» (Research Project No. 19-И23 dated December 8, 2022) (Registration number in the EGISU NIOKTR system (state accounting) – 123060500032-8).
Ethical Approval: The study was reviewed and approved by the Research Ethics Committee of the V.I. Kulakov NMRC for OG&P.
Patient Consent for Publication: All patients provided informed consent for the publication of their data.
Authors' Data Sharing Statement: The data supporting the findings of this study are available upon request from the corresponding author after approval from the principal investigator.
For citation: Kan N.E., Leonova A.A., Tyutyunnik V.L., Soldatova E.E., Ryzhova K.O., Serebriakova A.P.
Prediction of fetal growth restriction using machine learning algorithms.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (7): 40-46 (in Russian)
https://dx.doi.org/10.18565/aig.2025.135

fetal growth restriction

predictive model

logistic regression

random forest

machine learning

Full text (in Russian)

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Received 19.05.2025

Accepted 17.06.2025

Natalia E. Kan, Professor, Dr. Med. Sci., Honored Scientist of the Russian Federation, Deputy Director for Research – Director of the Institute of Obstetrics, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparina str., 4,
kan-med@mail.ru. Researcher ID: B-2370-2015, SPIN: 5378-8437, Authors ID: 624900, Scopus Author ID: 57008835600, https://orcid.org/0000-0001-5087-5946
Anastasia A. Leonova, PhD student, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparina str., 4, +7(937)453-54-27, nastena27-03@mail.ru, https://orcid.org/0000-0001-6707-3464
Victor L. Tyutyunnik, Professor, Dr. Med. Sci., Leading Researcher at the Center for Scientific and Clinical Research, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparina str., 4, tioutiounnik@mail.ru,
Researcher ID: B-2364-2015, SPIN: 1963-1359, Authors ID: 213217, Scopus Author ID: 56190621500, https://orcid.org/0000-0002-5830-5099
Ekaterina E. Soldatova, Researcher at the Obstetric Department of the Institute of Obstetrics, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparina str., 4, katerina.soldatova95@bk.ru, https://orcid.org/0000-0001-6463-3403
Kristina O. Ryzhova, Resident, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparina str., 4, cr.yanina@gmail.com, https://orcid.org/0009-0007-8318-435X
Anna P. Serebriakova, obstetrician-gynecologist at the Day Hospital Department, Primorsky Regional Perinatal Center, 690042, Russia, Vladivostok, Mozhayskaya st., 1B, serebriakovanna@gmail.com, https://orcid.org/0000-0001-7014-2627
Corresponding author: Anastasia A. Leonova, nastena27-03@mail.ru

Prediction of fetal growth restriction using machine learning algorithms

Kan N.E., Leonova A.A., Tyutyunnik V.L., Soldatova E.E., Ryzhova K.O., Serebriakova A.P.

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