Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Abstract

IntroductionMachine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.AimTo describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.MethodologyPublications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.Current stateML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.Future challengesTo improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.ConclusionThe use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Fetal Hypoxia Detection Using Machine Learning: A Narrative Review

“…Concurrently, DL methodologies, including ANN, CNN, and MLP, have delivered promising results in classifying fetal hypoxia cases. Nonetheless, the prevalent reliance on a single dataset in studies [25][26][27][28] highlights a significant research gap, underscoring the imperative of employing more diverse datasets to substantiate these models' validity. In the context of ensemble techniques, especially Gradient Boosting (GB) models, studies [41,[43][44][45] have yielded outstanding results.…”

“…Finally, Mennickent et al [25] presented the current level of knowledge about the application of ML to diseases and difficulties associated with pregnancy. The datasets in the ML model were typically from the biological area.…”

A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery

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