Barbounaki Stavroula, Vivilaki Victoria G
Department of Midwifery, School of Health and Care Sciences, University of West Attica, Athens, Greece.
Eur J Midwifery. 2021 Dec 20;5:58. doi: 10.18332/ejm/143166. eCollection 2021.
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.
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.
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.
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.
近年来，机器学习在各个领域（包括妇产科领域）越来越多地被用于开发能够表示和解决问题的模型。本系统评价的目的是分析关于机器学习和智能系统在助产和产科应用的研究。
在四个电子数据库（PubMed、APA PsycINFO、SCOPUS、ScienceDirect）中进行了全面的文献综述。本综述仅纳入讨论机器学习和智能系统在助产和产科应用的文章。对所选文章的相关性进行了严格评估，并进行了背景综合分析。
本系统评价纳入了32篇文章，因为它们符合本研究规定的纳入标准和方法学标准。结果表明，机器学习和智能系统在助产和产科的广泛主题（如诊断、妊娠风险评估、胎儿监测、膀胱肿瘤等）中产生了成功的模型和系统。
本系统评价表明，机器学习是人工智能中一个非常有前景的领域，可用于开发支持人类专家的实用且高效的应用程序，以及为进一步研究探索广泛的令人兴奋的机会。
