Abstract
Introduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications. Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.
Highlights
Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority
It is known that increasing obesity in women of childbearing age leads to increased risk of perinatal complications such as gestational diabetes, large for gestational age (LGA), fetal macrosomia, and hypertensive syndromes in pregnancy (Denison et al, 2010; Mariona, 2016; Edwards and Wright, 2020)
Search terms were chosen and searches performed in an iterative process, initially using word headings associated with Machine learning (ML), such as “machine learning,” “deep learning,” “artificial intelligence,” and related to perinatal complications, such as “complications in pregnancy” and “pregnancy complications,” and excluding articles related to postpartum and congenital complications
Summary
Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. It is known that increasing obesity in women of childbearing age leads to increased risk of perinatal complications such as gestational diabetes, large for gestational age (LGA), fetal macrosomia, and hypertensive syndromes in pregnancy (Denison et al, 2010; Mariona, 2016; Edwards and Wright, 2020). On the other hand, developed countries tend to see decreased birth rates over the years, leading to advanced gestational ages, predisposing women to adverse pregnancy outcomes (Laopaiboon et al, 2014)
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