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Machine Learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research
Journal article   Peer reviewed

Machine Learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

Contessa A Ricci, Benjamin Crysup, Nicole R Phillips, William C Ray, Mark K Santillan, Aaron J Trask, August E Woerner and Styliani Goulopoulou
American journal of physiology. Heart and circulatory physiology, Vol.327(2), pp.H417-H432
06/07/2024
DOI: 10.1152/ajpheart.00149.2024
PMCID: PMC11442027
PMID: 38847756

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Abstract

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Pregnancy thus poses physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies, and with far less known about pregnancy with complications. Further, current tools for prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML fundamentals and a discussion about platforms that can be used to enhance understanding of cardiovascular adaptations to pregnancy. Finally, we address the interpretability and explainability of ML outcomes, consequences of model bias, and ethics of ML use.The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Pregnancy thus poses physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies, and with far less known about pregnancy with complications. Further, current tools for prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML fundamentals and a discussion about platforms that can be used to enhance understanding of cardiovascular adaptations to pregnancy. Finally, we address the interpretability and explainability of ML outcomes, consequences of model bias, and ethics of ML use.
 artificial intelligence cardiovascular machine learning maternal health pregnancy

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