Machine learning and disease prediction in obstetrics

Zara Arain; Stamatina Iliodromiti; Gregory Slabaugh; Anna L David; Tina T Chowdhury

doi:10.1016/j.crphys.2023.100099

Machine learning and disease prediction in obstetrics

Curr Res Physiol. 2023 May 19:6:100099. doi: 10.1016/j.crphys.2023.100099. eCollection 2023.

Authors

Zara Arain¹, Stamatina Iliodromiti², Gregory Slabaugh³, Anna L David⁴, Tina T Chowdhury¹

Affiliations

¹ Centre for Bioengineering, School of Engineering and Materials Science, Queen Mary University of London, Mile End Road, London, E1 4NS, UK.
² Women's Health Research Unit, Wolfson Institute of Population Health, Queen Mary University of London, 58 Turner Street, London, E1 2AB, UK.
³ Digital Environment Research Institute, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 1HH, UK.
⁴ Elizabeth Garrett Anderson Institute for Women's Health, University College London, Medical School Building, Huntley Street, London, WC1E 6AU, UK.

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.

Keywords: Cardiotocography; Echocardiography; Gestational diabetes; Machine learning; Magnetic resonance imaging; Obstetrics; Pre-eclampsia; Preterm birth; Ultrasound.

Publication types

Review