Challenges of developing robust AI for intrapartum fetal heart rate monitoring

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dc.contributor.authorO'Sullivan, Mark E.
dc.contributor.authorConsidine, Elizabeth C.
dc.contributor.authorO'Riordan, Mairead
dc.contributor.authorMarnane, William P.
dc.contributor.authorRennie, J. M.
dc.contributor.authorBoylan, Geraldine B.
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2023-01-04T15:14:19Z
dc.date.available2023-01-04T15:14:19Z
dc.date.issued2021-10-26
dc.date.updated2023-01-03T13:22:07Z
dc.description.abstractBackground: CTG remains the only non-invasive tool available to the maternity team for continuous monitoring of fetal well-being during labour. Despite widespread use and investment in staff training, difficulty with CTG interpretation continues to be identified as a problem in cases of fetal hypoxia, which often results in permanent brain injury. Given the recent advances in AI, it is hoped that its application to CTG will offer a better, less subjective and more reliable method of CTG interpretation. Objectives: This mini-review examines the literature and discusses the impediments to the success of AI application to CTG thus far. Prior randomised control trials (RCTs) of CTG decision support systems are reviewed from technical and clinical perspectives. A selection of novel engineering approaches, not yet validated in RCTs, are also reviewed. The review presents the key challenges that need to be addressed in order to develop a robust AI tool to identify fetal distress in a timely manner so that appropriate intervention can be made. Results: The decision support systems used in three RCTs were reviewed, summarising the algorithms, the outcomes of the trials and the limitations. Preliminary work suggests that the inclusion of clinical data can improve the performance of AI-assisted CTG. Combined with newer approaches to the classification of traces, this offers promise for rewarding future development.en
dc.description.sponsorshipScience Foundation Ireland (19/FIP/AI/7483)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid765210en
dc.identifier.citationO'Sullivan, M. E., Considine, E. C., O'Riordan, M., Marnane, W. P., Rennie, J. M. and Boylan, G. B. (2021) 'Challenges of developing robust AI for intrapartum fetal heart rate monitoring', Frontiers in Artificial Intelligence, 4, 765210 (8pp) doi: 10.3389/frai.2021.765210en
dc.identifier.doi10.3389/frai.2021.765210en
dc.identifier.endpage8en
dc.identifier.issn2624-8212
dc.identifier.journaltitleFrontiers in Artificial Intelligenceen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14017
dc.identifier.volume4en
dc.language.isoenen
dc.publisherFrontiers Mediaen
dc.rights© 2021, O’Sullivan, Considine, O’Riordan, Marnane, Rennie and Boylan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectCardiotocography (CTG)en
dc.subjectFetal heart rate (FHR)en
dc.subjectHypoxic ischaemic encephalopathy (HIE)en
dc.subjectLabouren
dc.subjectPregnancyen
dc.subjectFetal hypoxiaen
dc.subjectArtificial intelligenceen
dc.subjectMachine learningen
dc.titleChallenges of developing robust AI for intrapartum fetal heart rate monitoringen
dc.typeArticle (peer-reviewed)en
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