Artificial intelligence and machine learning in cardiotocography: A scoping review.

Aeberhard, Jasmin L; Radan, Anda-Petronela; Delgado-Gonzalo, Ricard; Strahm, Karin Maya; Sigurthorsdottir, Halla Bjorg; Schneider, Sophie; Surbek, Daniel (2023). Artificial intelligence and machine learning in cardiotocography: A scoping review. European journal of obstetrics & gynecology and reproductive biology, 281, pp. 54-62. Elsevier 10.1016/j.ejogrb.2022.12.008

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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.

Item Type:

Journal Article (Review Article)

Division/Institute:

04 Faculty of Medicine > Department of Gynaecology, Paediatrics and Endocrinology (DFKE) > Clinic of Gynaecology

UniBE Contributor:

Radan, Anda-Petronela, Strahm, Karin Maya, Schneider, Sophie, Surbek, Daniel

Subjects:

600 Technology > 610 Medicine & health

ISSN:

0301-2115

Publisher:

Elsevier

Language:

English

Submitter:

Pubmed Import

Date Deposited:

20 Dec 2022 10:27

Last Modified:

08 Jan 2024 13:35

Publisher DOI:

10.1016/j.ejogrb.2022.12.008

PubMed ID:

36535071

Uncontrolled Keywords:

Artificial intelligence (AI) Cardiotocography (CTG) Fetal heart rate Fetal monitoring Labor Machine learning (ML) Obstetrics Pregnancy

BORIS DOI:

10.48350/176160

URI:

https://boris.unibe.ch/id/eprint/176160

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