Medical Faculty of the University of Bern, Switzerland.
Department of Obstetrics and Feto-maternal Medicine, University Hospital of Bern, Switzerland.
Eur J Obstet Gynecol Reprod Biol. 2023 Feb;281:54-62. doi: 10.1016/j.ejogrb.2022.12.008. Epub 2022 Dec 9.
Artificial intelligence (AI) is gaining more interest in the field of medicine due to its capacity to learn patterns directly from data. This becomes interesting for the field of cardiotocography (CTG) interpretation, since it promises to remove existing biases and improve the well-known issues of inter- and intra-observer variability.
The objective of this study was to map current knowledge in AI-assisted interpretation of CTG tracings and thus, to present different approaches with their strengths, gaps, and limitations. The search was performed on Ovid Medline and PubMed databases. The Preferred Reporting Items for Systematic Reviews and meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed.
We summarized 40 different studies investigating at least one algorithm or system to classify CTG tracings. In addition, the Oxford Sonicaid system is presented because of its wide use in clinical practice.
There are several promising approaches in this area, but none of them has gained big acceptance in clinical practice. Further investigation and refinement of the algorithms and features are needed to achieve a validated decision-support system. For this purpose, larger quantities of curated and labeled data may be necessary.
由于人工智能（AI）能够直接从数据中学习模式，因此它在医学领域越来越受到关注。这对于胎心监护（CTG）解释领域来说非常有趣，因为它有望消除现有的偏见并改善众所周知的观察者间和观察者内变异性问题。
本研究的目的是绘制当前人工智能辅助 CTG 描记分析的知识图谱，并展示不同方法的优势、差距和局限性。检索在 Ovid Medline 和 PubMed 数据库中进行。本研究遵循了系统评价和荟萃分析的首选报告项目（PRISMA-ScR）指南。
我们总结了 40 项不同的研究，这些研究至少调查了一种算法或系统来对 CTG 描记进行分类。此外，还介绍了牛津 Sonicaid 系统，因为它在临床实践中广泛使用。
在该领域有几个很有前途的方法，但没有一个在临床实践中得到广泛接受。需要进一步研究和改进算法和功能，以实现经过验证的决策支持系统。为此，可能需要更多经过整理和标记的数据。
