Machine learning applied in maternal and fetal health - A narrative review focused on pregnancy diseases and complications

Aug 5, 2026

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Machine learning methods use mathematics, statistics, and computation to learn from multiple variables and uncover hidden correlations for prediction. This narrative review summarizes the state of the art for applying machine learning to key pregnancy diseases and complications, including gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations. Searches in PubMed, Web of Science, and Google Scholar show diverse applications, especially prediction of perinatal disorders, along with biomarker discovery, risk estimation, and treatment or drug-related support. Future work emphasizes improved data handling, model interpretability, and validation.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

Aim

Methodology

Current state

Application areas

Future challenges

Conclusion

TYPE Review PUBLISHED 19 May 2023 DOI 10.3389/fendo.2023.1130139 OPEN ACCESS EDITED BY Lawrence Merle Nelson, Mary Elizabeth Conover Foundation, Inc., United States REVIEWED BY Shujie Liao, Huazhong University of Science and Technology, China Stepan Feduniw, University Zürich, Switzerland *CORRESPONDENCE Enrique Guzm´an-Gutie´rrez [eguzman@udec.cl](eguzman@udec.cl)[ ](eguzman@udec.cl)Juan Araya [jarayaq@udec.cl](jarayaq@udec.cl) RECEIVED 22 December 2022 ACCEPTED 04 May 2023 PUBLISHED 19 May 2023 CITATION Mennickent D, Rodrguez A, Opazo MC, Riedel CA, Castro E, Eriz-Salinas A, Appel-Rubio J, Aguayo C, Damiano AE, Guzm´an-Gutie´ rrez E and Araya J (2023) Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications. Front. Endocrinol. 14:1130139 . doi: 10.3389/fendo.2023.1130139 COPYRIGHT © 2023 Mennickent, Rodrguez, Opazo, Riedel, Castro, Eriz-Salinas, Appel-Rubio, Aguayo, Damiano, Guzm´an-Gutie´ rrez and Araya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications Daniela Mennickent 1,2,3, Andr´es Rodr ´ıguez 3,4, Ma. Cecilia Opazo 5,6, Claudia A. Riedel 6,7, Erica Castro 8, Alma Eriz-Salinas 9, Javiera Appel-Rubio 1, Claudio Aguayo 1, Alicia E. Damiano 10,11, Enrique Guzm´an-Guti´errez 1,3* and Juan Araya 2,3* 1 Departamento de Bioqumica Clnica e Inmunologa, Facultad de Farmacia, Universidad de Concepcio´n, Concepcio´n, Chile, 2 Departamento de An´alisis Instrumental, Facultad de Farmacia, Universidad de Concepcio´n, Concepcio´n, Chile, 3 Machine Learning Applied in Biomedicine (MLAB), Concepcio´n, Chile, 4 Departamento de Ciencias B´asicas, Facultad de Ciencias, Universidad del BíoBío, Chill´an, Chile, 5 Instituto de Ciencias Naturales, Facultad de Medicina Veterinaria y Agronoma, Universidad de Las Ame´ricas, Santiago, Chile, 6 Millennium Institute on Immunology and Immunotherapy, Santiago, Chile, 7 Departamento de Ciencias Biolo´gicas, Facultad de Ciencias de la Vida, Universidad Andre´s Bello, Santiago, Chile, 8 Departamento de Obstetricia y Puericultura, Facultadde Ciencias de la Salud, Universidad de Atacama, Copiapo´, Chile, 9 Departamento de Obstetricia y Puericultura, Facultad de Medicina, Universidad de Concepcio´n, Concepcio´n, Chile, 10C´atedra de Biologa Celular y Molecular, Departamento de Ciencias Biolo´gicas, Facultad de Farmacia y Bioqumica, Universidad de Buenos Aires, Buenos Aires, Argentina, 11 Laboratorio de Biologa de la Reproduccio´n, Instituto de Fisiologa y Biofsica Bernardo Houssay (IFIBIO-Houssay) -CONICET, Universidad de Buenos Aires, Buenos Aires, Argentina Introduction: Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to ﬁnd hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the ﬁeld of obstetrics and gynecology. Aim: To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications. Methodology: Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes

FAQs

What is the aim of this narrative review?

To describe the state of the art regarding the use of machine learning in pregnancy diseases and complications.

Which pregnancy conditions were considered in the review?

Gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

What are the main current uses of machine learning in these areas?

Machine learning is widely used, most commonly for predicting perinatal disorders, and also for biomarker discovery, risk estimation, correlation assessment, and supporting treatment or drug screening decisions.

What future challenges does the review highlight?

Improving data recording, storage, and updating; developing more accurate and understandable models with data from advanced instruments; and conducting validation and impact-analysis studies for existing high-accuracy models.

Aug 5, 2026

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