Artificial intelligence and machine learning in cardiotocography: A scoping review. (February 2023)

• Record Type: Journal Article
• Authors: Aeberhard, Jasmin L.; Radan, Anda-Petronela; Delgado-Gonzalo, Ricard; Strahm, Karin Maya; Sigurthorsdottir, Halla Bjorg; Schneider, Sophie; Surbek, Daniel
• Is Part Of: European journal of obstetrics, gynecology, and reproductive biology. Volume 281(2023)
• Page Start: 54
• Page End: 62
• Publication Date: 2023-02
• Subjects: ANN artificial neural network -- AI artificial intelligence -- AO adverse outcome -- CEEMDAN complete ensemble empirical mode decomposition with adaptive noise -- CNN convolutional neural network -- CTG cardiotocography -- DEC decelerations -- DSSAE deep stacked sparse auto-encoder -- DT decision tree -- GLCM gray level co-occurrence matrix -- IAGA improved adaptive genetic algorithm -- FHR fetal heart rate -- FLDA fishers linear discriminant analysis -- LNN legendre neural network -- LTV long-term variability -- ML machine learning -- MLA-ANFIS multi-layer architecture of an adaptive neuro fuzzy inference system -- NN neural network -- RF Random Forest -- RFE recursive feature elimination -- SMOTE Synthetic Minority Over-sampling Technique -- STFT short time Fourier transform -- STV short-term variability -- SVM support vector machine -- UC uterine contractions -- VNN volterra neural networks -- kNN kappa-nearest neighbor; Obstetrics -- Cardiotocography (CTG) -- Fetal monitoring -- Fetal heart rate -- Labor -- Pregnancy -- Artificial intelligence (AI) -- Machine learning (ML); Obstetrics -- Periodicals; Gynecology -- Periodicals; Reproductive health -- Periodicals; Gynecology -- Periodicals; Obstetrics -- Periodicals; Reproduction -- Periodicals; Obstétrique -- Périodiques; Gynécologie -- Périodiques; Reproduction -- Périodiques; Verloskunde; Gynaecologie; Voortplanting (biologie); Gynecology; Obstetrics; Reproduction; Electronic journals; Periodicals; Electronic journals; 618.05
• DOI: 10.1016/j.ejogrb.2022.12.008
• Languages: English
• ISSNs: 0301-2115

Abstract

Abstract: 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.