Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

Frontiers in Artificial Intelligence

M. O'Riordan¹  G. B. Boylan²  E. C. Considine³  M. E. O’Sullivan³  J. M. Rennie⁴  W. P. Marnane⁵ 

[1] Department Obstetrics and Gynaecology, University College Cork, Cork, Ireland;Department of Paediatrics and Child Health, University College Cork, Cork, Ireland;INFANT Research Centre, University College Cork, Cork, Ireland;Institute for Women’s Health, University College London, London, United Kingdom;School of Engineering, University College Cork, Cork, Ireland;

关键词: cardiotocography (CTG);    fetal heart rate (FHR);    hypoxic ischaemic encephalopathy (HIE);    labour;    pregnancy;    fetal hypoxia;   

DOI  :  10.3389/frai.2021.765210

来源: DOAJ

【 摘 要 】

Background: 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.

【 授权许可】

Unknown