Challenges of Developing Robust AI for Intrapartum Fetal Heart Rate Monitoring

Abstract

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.

CTGNet: Automatic Analysis of Fetal Heart Rate from Cardiotocograph Using Artificial Intelligence

“…With advances in Artificial Intelligence (AI), all the features, including morphological features, can now be extracted by using deep learning-based approaches. Deep learning models are also expected to solve the above problem of circular definitions 6,27–29 . This paper proposes a method of FHR analysis that considers the morphological characteristics of the signals and uses traditional methods of filtering.…”

“…Deep learning models are also expected to solve the above problem of circular definitions. 6,[27][28][29] This paper proposes a method of FHR analysis that considers the morphological characteristics of the signals and uses traditional methods of filtering. The proposed method is used to monitor and analyze data on women in labor, and the results show that it is superior to traditional methods.…”

Computerized cardiotocography analysis during labor – A state‐of‐the‐art review

“…The latest systems published, based on ML and DL rather than on the FIGO's guidelines, show promising results when evaluated on retrospective cohorts, 13,14 but many challenges remain to be solved before they can be used in clinical practice. 15 Here, we present a state-of-the-art review of computerized CTG analysis, with a particular focus on the systems based on the latest ML and DL approaches and released in the very last years. We conclude with our thoughts on the challenges ahead to use those systems in clinical practice.…”

“…Also, the recent emergence of machine learning (ML) and deep learning (DL), and their successful application to similar topics such as computerized analysis of electrocardiograms 11 or electroencephalograms, 12 gave researchers new efficient statistical tools to assist in CTG analysis. The latest systems published, based on ML and DL rather than on the FIGO's guidelines, show promising results when evaluated on retrospective cohorts, 13,14 but many challenges remain to be solved before they can be used in clinical practice 15 …”

“…The latest systems published, based on ML and DL rather than on the FIGO's guidelines, show promising results when evaluated on retrospective cohorts, 13 , 14 but many challenges remain to be solved before they can be used in clinical practice. 15 …”