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Review
. 2021 Dec 20:5:58.
doi: 10.18332/ejm/143166. eCollection 2021.

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Stavroula Barbounaki  1 , Victoria G Vivilaki  1
Affiliations
Review

Intelligent systems in obstetrics and midwifery: Applications of machine learning

Stavroula Barbounaki et al. Eur J Midwifery. .

Abstract

Introduction: Machine learning is increasingly utilized over recent years in order to develop models that represent and solve problems in a variety of domains, including those of obstetrics and midwifery. The aim of this systematic review was to analyze research studies on machine learning and intelligent systems applications in midwifery and obstetrics.

Methods: A thorough literature review was performed in four electronic databases (PubMed, APA PsycINFO, SCOPUS, ScienceDirect). Only articles that discussed machine learning and intelligent systems applications in midwifery and obstetrics, were considered in this review. Selected articles were critically evaluated as for their relevance and a contextual synthesis was conducted.

Results: Thirty-two articles were included in this systematic review as they met the inclusion and methodological criteria specified in this study. The results suggest that machine learning and intelligent systems have produced successful models and systems in a broad list of midwifery and obstetrics topics, such as diagnosis, pregnancy risk assessment, fetal monitoring, bladder tumor, etc.

Conclusions: This systematic review suggests that machine learning represents a very promising area of artificial intelligence for the development of practical and highly effective applications that can support human experts, as well the investigation of a wide range of exciting opportunities for further research.

Keywords: diagnosis; intelligent systems; machine learning; midwifery; obstetrics; pregnancy.

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Conflict of interest statement

The authors have each completed and submitted an ICMJE Form for Disclosure of Potential Conflicts of Interest. The authors declare that they have no competing interests, financial or otherwise, related to the current work. V. G. Vivilaki reports that she is the Editor-in-Chief of the EJM journal.

Figures

Figure 1
Figure 1
The process for identifying and selecting the articles for the systematic review
See this image and copyright information in PMC

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