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Machine learning and disease prediction in obstetrics

Arain, Zara; Iliodromiti, Stamatina; Slabaugh, Gregory; David, Anna L; Chowdhury, Tina T; (2023) Machine learning and disease prediction in obstetrics. Current Research in Physiology , 6 , Article 100099. 10.1016/j.crphys.2023.100099. Green open access

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Abstract

Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.

Type: Article
Title: Machine learning and disease prediction in obstetrics
Location: Netherlands
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.crphys.2023.100099
Publisher version: https://doi.org/10.1016/j.crphys.2023.100099
Language: English
Additional information: © 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Keywords: Cardiotocography, Echocardiography, Gestational diabetes, Machine learning, Magnetic resonance imaging, Obstetrics, Pre-eclampsia, Preterm birth, Ultrasound
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
URI: https://discovery.ucl.ac.uk/id/eprint/10172563
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