Artificial intelligence and machine learning in cardiotocography: A scoping review

Published:December 08, 2022DOI:



      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.


      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.


      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)


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