ISSN 0300-9092 (Print)
ISSN 2412-5679 (Online)

Comparative analysis of the accuracy of ultrasonography and magnetic resonance imaging in estimating fetal weight

Syrkashev E.M., Nikolaeva A.V., Stoliarova E.V., Kholin A.M., Gorina K.A., Kesova M.I., Baev O.R., Kan N.E., Gus A.I.

1) Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of the Russia, Moscow, Russia; 2) I.M. Sechenov First Moscow State Medical University, Ministry of Health of Russia (Sechenov University), Moscow, Russia; 3) Patrice Lumumba Peoples' Friendship University of Russia, Moscow, Russia

Objective: To compare the accuracy of ultrasonography (USG) and magnetic resonance imaging (MRI) in determining estimated fetal weight (EFW).
Materials and methods: This prospective study included 103 pregnant women who underwent both MRI and USG before delivery. The EFW based on MRI data was calculated using the formula by Baker et al., while the EFW based on USG data was calculated using the Hadlock et al. formula. The EFW values were assessed using absolute measurements and on a percentile scale (INTERGROWTH-21st).
Results: The correlation coefficient between EFW based on USG data and the newborn's birth weight was 0.831 (p<0.001), while for MRI, it was 0.941 (p<0.001). The mean absolute error (MAE) of EFW in absolute values for USG was 145.68 (427.42) g, and for MRI, it was 117.83 (221.98) g, on a percentile scale, the MAE for USG was 4.17 (15.68), for MRI, it was 3.16 (7.03). The correlation coefficient between EFW above the 90th percentile was 0.374 (p=0.041) for USG and 0.855 (p<0.001) for MRI. The MAE for determining EFW (>90th percentile) was 173.93 (432.16) g for USG and 122.0 (202.82) g for MRI. On a percentile scale, the MAE was 0.38 (6.07) for USG and 0.76 (2.56) for the MRI. The area under the curve (ROC AUC) for identifying cases with birth weights > 4000 g was 0.916 (95% CI: 0.860–0.973) for USG and 0.986 (95% CI: 0.967–1.000) for MRI.
Conclusion: EFW determination based on MRI data is more accurate than that based on USG data, with the most significant differences noted in cases of fetal macrosomia. Developing machine learning algorithms is essential to reduce the time required for segmenting areas of interest, thereby enhancing the role of artificial intelligence in automating the EFW determination processes. Further research is necessary to establish the optimal timing and indications for using MRI as an additional method for determining the EFW.

Authors' contributions: Syrkashev E.M. – concept development, conducting research, text writing; Nikolaeva A.V. – recruitment of patients into the study, conducting research, text editing; Stoliarova E.V. – recruitment of patients into the study, collection and analysis of initial data; Kholin A.M. – concept development, conducting research, collection and analysis of initial data, editing; Gorina K.A., Kesova M.I. – recruitment of patients into the study; Baev O.R. – research management, recruitment of patients into the study, interpretation of the results obtained, text editing; Kan N.E. – research management, interpretation of the results obtained, text editing; Gus A.I. – research management, concept development, conducting research, text editing.
Conflicts of interest: The authors have no conflicts of interest to declare.
Funding: There was no funding for this study.
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: Syrkashev E.M., Nikolaeva A.V., Stoliarova E.V., Kholin A.M., Gorina K.A., Kesova M.I., 
Baev O.R., Kan N.E., Gus A.I. Comparative analysis of the accuracy of ultrasonography and 
magnetic resonance imaging in estimating fetal weight.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (9): 82-88 (in Russian)
https://dx.doi.org/10.18565/aig.2025.152

Keywords

estimated fetal weight
prenatal diagnosis
fetal MRI
USG
macrosomia

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Received 11.06.2025

Accepted 01.09.2025

About the Authors

Egor M. Syrkashev, PhD, Senior Researcher at the Radiology Department, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4, e_syrkashev@oparina4.ru, https://orcid.org/0000-0003-4043-907X
Anastasia V. Nikolaeva, PhD, Chief Physician, V V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4,
a_nikolaeva@oparina4.ru, https://orcid.org/0000-0002-0012-6688
Elizaveta V. Stoliarova, PhD student, 1st Obstetric Department of Pregnancy Pathology, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4, ev_stolyarova@oparina4.ru, https://orcid.org/0009-0001-2049-3119
Alexey M. Kholin, PhD, Head of the Department of Telemedicine, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4, a_kholin@oparina4.ru, https://orcid.org/0000-0002-4068-9805
Ksenia A. Gorina, PhD, Junior Researcher at the 1 Department of Obstetric Pathology of Pregnancy, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4, k_gorina@oparina4.ru, https://orcid.org/0000-0001-6266-2067
Marina I. Kesova, Dr. Med. Sci., Senior Researcher at the Obstetric Department, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4, m_kesova@oparina4.ru, https://orcid.org/0000-0001-7764-8073
Oleg R. Baev, Dr. Med. Sci., Professor, Head of the 1st Maternity Department, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4; Professor at the Department of Obstetrics, Gynecology, Perinatology, and Reproductology, I.M. Sechenov First MSMU,
Ministry of Health of Russia, 119991, Russia, Moscow, Trubetskaya str., 8-2, o_baev@oparina4.ru, https://orcid.org/0000-0001-8572-1971
Natalia E. Kan, Dr. Med. Sci., Professor, Deputy Director for Science, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia,
117997, Russia, Moscow, Ac. Oparin str., 4, kan-med@mail.ru, https://orcid.org/0000-0001-5087-5946
Aleksandr I. Gus, Dr. Med. Sci., Chief Researcher at the Department of Ultrasound and Functional Diagnostics, V.I. Kulakov NMRC for OG&P, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4; Head of the Department of Ultrasound Diagnostics, Medical Institute, Patrice Lumumba Peoples’ Friendship University of Russia, 127015, Russia, Moscow, Pistsovaya str., 10, a_gus@oparina4.ru, https://orcid.org/0000-0003-1377-3128

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