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Computerized cardiotocography analysis during labor – A state‐of‐the‐art review
Cardiotocography is defined as the recording of fetal heart rate and uterine contractions and is widely used during labor as a screening tool to determine fetal wellbeing. The visual interpretation of the cardiotocography signals by the practitioners, following common guidelines, is subject to a hig...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889319/ https://www.ncbi.nlm.nih.gov/pubmed/36541016 http://dx.doi.org/10.1111/aogs.14498 |
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author | Ben M'Barek, Imane Jauvion, Grégoire Ceccaldi, Pierre‐François |
author_facet | Ben M'Barek, Imane Jauvion, Grégoire Ceccaldi, Pierre‐François |
author_sort | Ben M'Barek, Imane |
collection | PubMed |
description | Cardiotocography is defined as the recording of fetal heart rate and uterine contractions and is widely used during labor as a screening tool to determine fetal wellbeing. The visual interpretation of the cardiotocography signals by the practitioners, following common guidelines, is subject to a high interobserver variability, and the efficiency of cardiotocography monitoring is still debated. Since the 1990s, researchers and practitioners work on designing reliable computer‐aided systems to assist practitioners in cardiotocography interpretation during labor. Several systems are integrated in the monitoring devices, mostly based on the guidelines, but they have not clearly demonstrated yet their usefulness. In the last decade, the availability of large clinical databases as well as the emergence of machine learning and deep learning methods in healthcare has led to a surge of studies applying those methods to cardiotocography signals analysis. The state‐of‐the‐art systems perform well to detect fetal hypoxia when evaluated on retrospective cohorts, but several challenges remain to be tackled before they can be used in clinical practice. First, the development and sharing of large, open and anonymized multicentric databases of perinatal and cardiotocography data during labor is required to build more accurate systems. Also, the systems must produce interpretable indicators along with the prediction of the risk of fetal hypoxia in order to be appropriated and trusted by practitioners. Finally, common standards should be built and agreed on to evaluate and compare those systems on retrospective cohorts and to validate their use in clinical practice. |
format | Online Article Text |
id | pubmed-9889319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98893192023-02-02 Computerized cardiotocography analysis during labor – A state‐of‐the‐art review Ben M'Barek, Imane Jauvion, Grégoire Ceccaldi, Pierre‐François Acta Obstet Gynecol Scand Review Cardiotocography is defined as the recording of fetal heart rate and uterine contractions and is widely used during labor as a screening tool to determine fetal wellbeing. The visual interpretation of the cardiotocography signals by the practitioners, following common guidelines, is subject to a high interobserver variability, and the efficiency of cardiotocography monitoring is still debated. Since the 1990s, researchers and practitioners work on designing reliable computer‐aided systems to assist practitioners in cardiotocography interpretation during labor. Several systems are integrated in the monitoring devices, mostly based on the guidelines, but they have not clearly demonstrated yet their usefulness. In the last decade, the availability of large clinical databases as well as the emergence of machine learning and deep learning methods in healthcare has led to a surge of studies applying those methods to cardiotocography signals analysis. The state‐of‐the‐art systems perform well to detect fetal hypoxia when evaluated on retrospective cohorts, but several challenges remain to be tackled before they can be used in clinical practice. First, the development and sharing of large, open and anonymized multicentric databases of perinatal and cardiotocography data during labor is required to build more accurate systems. Also, the systems must produce interpretable indicators along with the prediction of the risk of fetal hypoxia in order to be appropriated and trusted by practitioners. Finally, common standards should be built and agreed on to evaluate and compare those systems on retrospective cohorts and to validate their use in clinical practice. John Wiley and Sons Inc. 2022-12-20 /pmc/articles/PMC9889319/ /pubmed/36541016 http://dx.doi.org/10.1111/aogs.14498 Text en © 2022 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG). https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Review Ben M'Barek, Imane Jauvion, Grégoire Ceccaldi, Pierre‐François Computerized cardiotocography analysis during labor – A state‐of‐the‐art review |
title | Computerized cardiotocography analysis during labor – A state‐of‐the‐art review |
title_full | Computerized cardiotocography analysis during labor – A state‐of‐the‐art review |
title_fullStr | Computerized cardiotocography analysis during labor – A state‐of‐the‐art review |
title_full_unstemmed | Computerized cardiotocography analysis during labor – A state‐of‐the‐art review |
title_short | Computerized cardiotocography analysis during labor – A state‐of‐the‐art review |
title_sort | computerized cardiotocography analysis during labor – a state‐of‐the‐art review |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9889319/ https://www.ncbi.nlm.nih.gov/pubmed/36541016 http://dx.doi.org/10.1111/aogs.14498 |
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