Imperial College Healthcare NHS Trust

Government Agency (Health and Adult Social Care)

Deirdre Lyons

Unknown

No

NIBDAPC_2021_0005

This study involves the expansion of the retrospective collection of routine data from women in labour at Imperial College Healthcare NHS Trust Maternity Units between the years of 2015 and 2023. This part of the study is retrospective analysis, thus will not impact on the quality of care that was provided nor will it introduce discrepancies in treatment options. The ultimate aim of the study is to create an algorithm, through machine learning/ artificial intelligence that will improve recognition and management of abnormal foetal heart traces in future (Artificial Intelligence is a set of instructions which are written in a computer program. The instructions run a computer programme which performs mathematical tests on data. The instructions that allow the AI to work are called an ‘algorithm’). This study will principally consist of the collection of foetal heart rate tracings, which are stored in digital form, from women in labour. Foetal heart rate monitoring is used to monitor foetal well being in labour. In addition to this data, we will link this to de-identified (non-identifiable with any patient) patient-level information regarding the maternal and foetal outcomes. This collected and linked data will be used to train, validate and subsequently test an artificial intelligence-enabled model for the identification of features that occur in abnormal heart rate tracings or patterns. Some of these patterns may not always be easily detectable. It can then be translated into decision support for clinicians undertaking care of women in labour, to identify abnormalities more quickly in labour. An automated and reliable Artificial Intelligence based tool will reduce human error leading to improved health benefits and reduction of adverse outcomes for babies and mothers in labour. Issues related to use of patient information is mitigated through the use of de-identified information at the point of extraction as well as analysis and the use of secure servers of Imperial College Healthcare NHS Trust and the Big Data Analytic Unit (at Imperial College London) to link and store, as well as analyse this data, respectively. The initial Pilot data has allowed the team to develop a process of data management and also has shown potential for development of a novel machine learning process (algorithm). This however was Pilot data on 100 patients only and to show transferable results to a wider population, much more data is required to ensure development of a safe and robust machine learning process to improve earlier identification of abnormalities in foetal heart rate tracings.

It is known that approximately half of all stillbirths and a quarter of neonatal deaths result from complications during labour and childbirth, which remains a major concern for women, their families and the healthcare professionals who are tasked with providing gold-standard care for them. A significant contributory factor to this is misinterpretation of foetal heart monitoring. In addition, misinterpretation of foetal heart monitoring results in inappropriate intervention by caesarean section and instrumental deliveries, as well as being associated with maternal and foetal morbidity and mortality. An artificial intelligence system that can more specifically interpret and predict the foetal tracings will address the concerns above and therefore will hold the potential to improve outcomes for women and children. This study aims to provide population level benefits in the coming years for both high and low resource settings.

Public Health Research

24/09/2021

24/09/2021

De-Personalised

Not applicable

Not applicable

One-off

24/03/2025

TRE