Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes.
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Abstract
Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.
We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes.
We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (nā=ā69) than unsupervised methods (nā=ā9). Popular methods included support vector machines (nā=ā30), artificial neural networks (nā=ā22), regression analysis (nā=ā17) and random forests (nā=ā16). Methods such as DL are beginning to gain traction (nā=ā13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (nā=ā73); perinatal care, birth and delivery (nā=ā20); and preterm birth (nā=ā13). Efforts to translate AI into clinical care include clinical decision support systems (nā=ā24) and mobile health applications (nā=ā9).
Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (nā=ā13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (nā=ā2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.
Ā© The Author(s) 2021. Published by Oxford University Press.