Artificial intelligence and machine learning in cardiotocography: A scoping review

Introduction: 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. Material and methods: 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. Results: 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. Conclusions: 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.

Automated summary

Artificial intelligence has gained increasing interest in the field of medicine, particularly in the context of cardiotocography (CTG) interpretation, due to its ability to learn patterns directly from data and potentially remove biases and improve observer variability. This study presents an overview of different approaches and their strengths, gaps, and limitations in AI-assisted CTG interpretation. While there are promising approaches, none of them have gained widespread acceptance in clinical practice, highlighting the need for further investigation and refinement of algorithms and features.