The possibilities of using machine learning and artificial intelligence methods for morphological analysis of the placenta

DOI

https://dx.doi.org/10.18565/aig.2025.127

Tumanova U.N., Tumanov N.A., Shchegolev A.I., Serov V.N.
Academician V.I. Kulakov National Medical Research Centre for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, Moscow, Russia

Morphological examination of the placenta is an essential part of its pathology examination as it is possible to identify pathological processes and lesions that may recur in subsequent pregnancies.
The aim of the work is to analyze literature data and the results of our own research on the possibilities of using machine analysis and artificial intelligence to assess morphological changes in the placenta.
The available literature data demonstrate the potential of applying methods of digital pathology, machine learning, and artificial intelligence. Currently, there are three main areas of morphological studies into the placenta: automated analysis of histological samples, identification of cell and tissue types, and determination of any existing lesions or pathological processes. The paper describes the possibilities of recognizing and differentiating 11 types of cells and 9 types of tissue structures, including 5 types of villi. It also discusses the opportunity to detect individual or group lesions such as villous infarction, intervillous space thrombosis, decidual vasculopathy, and chorioamnionitis. The article provides the information on the differential diagnosis of morphological changes in the placenta during preeclampsia and fetal growth retardation.
Conclusion: The analysis of literature data showed that machine (computer) analysis and artificial intelligence have a number of advantages over conventional morphological studies for morphological analysis of the placenta: the analysis of the images requires a shorter period of time, there is a quantitative and independent evaluation of the entire image at once, rather than separate fields of view at different magnifications, machine learning algorithms and artificial intelligence can be used to obtain a conclusion. It is necessary to continue developing and implement these methods in practice on a large scale.

Authors’ contributions: Tumanova U.N., Tumanov N.A. – search for publications, analysis of publication data, writing the text of the manuscript; Shchegolev A.I. – developing the design of the study, analysis of publication data, writing the manuscript text; Serov V.N. – developing the design of the study, analysis of publication data, editing the manuscript text.
Conflicts of interest: The authors declare that there are no conflicts of interest.
Funding: The study was carried out without sponsorship.
For citation: Tumanova U.N., Tumanov N.A., Shchegolev A.I., Serov V.N. The possibilities of
using machine learning and artificial intelligence methods for morphological analysis of the placenta.
Akusherstvo i Ginekologiya/Obstetrics and Gynecology. 2025; (6): 84-93 (in Russian)
https://dx.doi.org/10.18565/aig.2025.127

placenta

morphology

pathology

machine learning

artificial intelligence

Full text (in Russian)

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Received 14.05.2025

Accepted 02.06.2025

Uliana N. Tumanova, Dr. Med. Sci., Leading Researcher at the 2nd Pathology Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, +7(495)531-44-44, thanatoradiology@gmail.com,
https://orcid.org/0000-0002-0924-6555
Nikita A. Tumanov, Master’s student, MIREA – Russian Technological University, 119454, Russia, Moscow, Vernadskogo Ave., 78, toomuchick@gmail.com
Alexander I. Shchegolev, Dr. Med. Sci., Professor, Head of the 2nd Pathology Department, Academician V.I. Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow, Ac. Oparin str., 4, +7(495)531-44-44, ashegolev@oparina4.ru,
https://orcid.org/0000-0002-2111-1530
Vladimir N. Serov, Dr. Med. Sci., Professor, Academician of RAS, President of Russian Society of Obstetricians and Gynecologists, Chief Scientific Consultant,
Academician V.I. Kulakov National Medical Research Center of Obstetrics, Gynecology, and Perinatology, Ministry of Health of Russia, 117997, Russia, Moscow,
Ac. Oparin str., 4, v_serov@oparina4.ru

The possibilities of using machine learning and artificial intelligence methods for morphological analysis of the placenta

Tumanova U.N., Tumanov N.A., Shchegolev A.I., Serov V.N.

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