Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, 1216, Bangladesh.
BMC Pregnancy Childbirth. 2022 Apr 22;22(1):348. doi: 10.1186/s12884-022-04594-2.
Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach. As outcomes, this review study highlighted the objectives or focuses of the recent studies conducted on pregnancy outcomes using ML; explored the adopted ML algorithms along with their performances; and provided a synthesized view of features used, types of features, data sources and its characteristics. Besides, the review investigated and depicted how the objectives of the prior studies have changed with time being; and the association among the objectives of the studies, uses of algorithms, and the features. The study also delineated future research opportunities to facilitate the existing initiatives for reducing maternal complacent and mortality rates, such as: utilizing unsupervised and deep learning algorithms for prediction, revealing the unknown reasons of maternal complications, developing usable and useful ML-based clinical decision support systems to be used by the expecting mothers and health professionals, enhancing dataset and its accessibility, and exploring the potentiality of surgical robotic tools. Finally, the findings of this review study contributed to the development of a conceptual framework for advancing the ML-based maternal healthcare system. All together, this review will provide a state-of-the-art paradigm of ML-based maternal healthcare that will aid in clinical decision-making, anticipating pregnancy problems and delivery mode, and medical diagnosis and treatment.
机器学习 (ML) 在预测分娩方式和评估妊娠期间潜在的产妇风险方面得到了广泛应用。本综述研究的主要目的是探索利用 ML 技术预测最佳分娩方式和检测分娩过程中各种并发症的当前研究和开发视角。共有 26 篇文章（发表于 2000 年至 2020 年之间）从最初的 241 篇文章中通过系统文献综述 (SLR) 方法中筛选和回顾。作为结果，本综述研究强调了使用 ML 研究妊娠结局的近期研究的目标或重点；探讨了采用的 ML 算法及其性能；并综合了使用的特征、特征类型、数据源及其特征。此外，该综述调查并描述了先前研究的目标随时间的变化情况；以及研究目标、算法使用和特征之间的关联。该研究还描绘了未来的研究机会，以促进现有的减少产妇并发症和死亡率的计划，例如：利用无监督和深度学习算法进行预测，揭示产妇并发症的未知原因，开发可用于预期母亲和卫生专业人员使用的基于 ML 的临床决策支持系统，增强数据集及其可访问性，并探索手术机器人工具的潜力。最后，本综述研究的结果为推进基于 ML 的产妇医疗保健系统的发展做出了贡献。总的来说，本综述将提供一个基于 ML 的产妇医疗保健的最新范例，将有助于临床决策、预测妊娠问题和分娩方式以及医疗诊断和治疗。
