Skip to main content

Call for papers - Machine learning to build predictive models in maternal-fetal medicine

Guest Editors

Tess Cersonsky, MD, Icahn School of Medicine at Mount Sinai, USA
Alyssa Hochberg, MD, MPH, McGill University, Canada

Submission Status: Open   |   Submission Deadline: 13 February 2025

BMC Pregnancy and Childbirth is calling for submissions to our Collection on Machine learning to build predictive models in maternal-fetal medicine. 

This Collection aims to advance maternal-fetal medicine by leveraging innovations in machine learning technologies. Maternal-fetal medicine seeks to safeguard the health of both mother and unborn child during pregnancy and childbirth, yet despite medical advancements, complications persist, leading to adverse outcomes like maternal mortality and neonatal morbidity. The integration of machine learning offers a promising avenue to harness clinical data for predictive modeling, empowering healthcare professionals to detect subtle risk factors that conventional diagnostics may overlook.

New Content ItemThis Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.

Meet the Guest Editors

Back to top

Tess Cersonsky, MD, Icahn School of Medicine at Mount Sinai, USA

Dr Cersonsky is a resident at Mount Sinai Hospital (Icahn School of Medicine, NY) in the Department of Obstetrics, Gynecology, and Reproductive Sciences. Her research interests include the use of machine learning for predicting adverse pregnancy outcomes, placental disorders, and psychological sequelae of adverse pregnancy events. Her background in biomedical engineering and biomedical informatics has led her to apply machine learning and computational modeling to obstetric phenomena.

Alyssa Hochberg, MD, MPH, McGill University, Canada

Alyssa Hochberg, MD, MPH, completed her Obstetrics & Gynecology residency in 2021 with distinction at Rabin Medical Center and Tel Aviv University, Israel. She is currently completing her postgraduate training in Reproductive Endocrinology and Infertility at McGill University, Canada. Her special research interests are obstetrical outcomes following IVF, and laboratory, obstetric, and perinatal outcomes of poor and high responders.    

About the Collection

BMC Pregnancy and Childbirth is calling for submissions to our Collection on Machine learning to build predictive models in maternal-fetal medicine. 

Maternal-fetal medicine encompasses a broad spectrum of healthcare aimed at ensuring the well-being of both the mother and the unborn child during pregnancy and childbirth. Despite significant advancements in medical technology and obstetric care, maternal and fetal complications continue to pose substantial challenges, contributing to adverse outcomes such as maternal mortality, stillbirths, and neonatal morbidity. With the advent of machine learning technologies, there is a significant opportunity to leverage vast amounts of clinical data to develop predictive models that can aid healthcare professionals to identify subtle patterns and predictors of adverse outcomes that may elude conventional diagnostic approaches.

BMC Pregnancy and Childbirth is launching a new Collection, Machine learning to build predictive models in maternal-fetal medicine, to facilitate the understanding of how machine learning algorithms can enhance clinical decision-making, improve patient outcomes, and ultimately reduce maternal and fetal morbidity and mortality rates. The Collection invites researchers and clinicians in fields including maternal-fetal medicine, high-risk obstetrics, midwifery, gynecology, perinatology, computer science, and statistics to contribute research that explores topics including, but not limited to, the prediction of gestational complications, fetal anomaly detection, preterm birth forecasting, preeclampsia risk assessment, anticipation of maternal hemorrhage, neonatal outcome prediction, integration of multi-modal data, and the development of clinical decision support systems using machine learning techniques in maternal-fetal medicine.

This Collection supports and amplifies research related to SDG 3: Good Health and Well-Being.

Image credit: © ryanking999 / stock.adobe.com

  1. Newborns are shaped by prenatal maternal experiences. These include a pregnant person’s physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a serie...

    Authors: Robert D. Henry
    Citation: BMC Pregnancy and Childbirth 2024 24:603
    Content type: Research Published on:

  2. We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.

    Authors: Yeliz Kaya, Zafer Bütün, Özer Çelik, Ece Akça Salik, Tuğba Tahta and Arzu Altun Yavuz
    Citation: BMC Pregnancy and Childbirth 2024 24:574
    Content type: Research Published on:

Submission Guidelines

Back to top

This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read the submission guidelines to confirm that type is accepted by the journal you are submitting to. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Machine learning to build predictive models in maternal-fetal medicine" from the dropdown menu.

Articles will undergo the standard peer-review process of the journal and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.