Deep Learning Models for Fetal Monitoring and Decision Support in Labour

Oxygen reaches the baby in the mother womb via the placenta and umbilical cord. During labour, the contractions squeeze the placenta and the cord, reducing the supply of oxygen to the baby. Most healthy babies cope well, but a small percentage are at risk of suffocation and brain injury. This causes each year in the UK about 100 healthy babies to die and more than 1,000 to sustain brain injury. Globally, each year, events during childbirth are estimated to account for 920,000 neonatal deaths; 1.1 million stillbirths during labour; and more than a million babies per year develop different important sequelae, from cerebral palsy to mental retardation, learning difficulties and other disabilities.
A baby which is at higher risk can be delivered urgently by emergency Caesarean section or instrumental vaginal extraction. To monitor the baby during labour, midwives and doctors in the developed countries use the cardiotocogram (CTG), which continuously displays the womb's contractions and the baby's heart rate. But our understanding of how to read the complex CTG graphs is limited and the patterns are difficult to interpret by eye, so some babies end up injured while at the same time many unnecessary emergency interventions are performed. Nearly 50% of the NHS litigation bill relates to maternity claims (in 2000-2010 these amounted to £3.1bn) and the majority of these are related to shortcomings in labour management and CTG interpretation.
With currently available computing power and routinely collected clinical data, methods using intelligent computer-based analysis can establish the relation of the CTG patterns and other clinical factors during labour to the baby's health at birth. In our pioneering work, we have already derived from the data a first prototype of a basic automated CTG analysis model, demonstrating proof-of-concept of using routinely collected maternal-fetal clinical data from pregnancy and childbirth (using retrospectively the data of more than 22,000 births at term). More recently, using the data from over 35,000 births at term, we conducted the first ever work on the application of deep learning methods for the analysis of CTG data, demonstrating their capability to learn from the raw CTG data and supersede all prior automated algorithms.
The main goal of this proposal is, based on an updated and unique Oxford dataset of over 100,000 births, to develop innovative deep learning models for personalised continuous fetal health risk assessment during labour. In particular, we propose to: (1) develop automated models for continuous fetal heart rate analysis based on the CTG data and clinical risk factors, incorporating convolutional neural networks and long short term memory networks into multimodal and stacked models; (2) develop a software App for tablets, capable of real-time CTG analysis and risk assessment of fetal compromise; (3) validate and demonstrate the capability, accuracy, and efficiency of the new models running on the App, by conducting simulations with real-time data at the Oxford NHS Trust as well as with retrospective data.
The output of this proposal will constitute a crucial building block towards the team's overarching goal to deliver at the bedside an individualised data-based tool for clinical decision support, preventing brain injury of the baby during labour. The main output will be a decision-support tool ready for prospective clinical tests, which holds clear potential benefits for the individuals, society, clinicians, and the NHS. It will address unresolved challenges in this clinical field, where improvements are painfully needed and are long overdue. This project is timely and represents excellent value for money, given the existing database and substantial prior work, the enormity of NHS litigation claims and the costs of unnecessary operative deliveries.

Monitoring reliably and consistently the health of babies during childbirth remains a massive unmet clinical need worldwide. In the UK alone, over 1200 otherwise healthy term babies sustain permanent brain injury during labour. Recent reports have found that in the UK, problems with monitoring the fetal health during labour were a factor in over half of the infants with potentially preventable brain injury at term (Royal College of Obstetricians and Gynaecologists. Each Baby Counts: 2018 Progress Report. London: RCOG, 2018).
Nearly 50% of the NHS litigation bill relates to maternity claims, and between the years 2000 and 2010 these amounted to £3.1bn (The NHS Litigation Authority. Ten years of maternity claims: an analysis of NHS Litigation Authority data, 2012). The majority of these claims related to shortcomings in labour management and cardiotocgraphy (CTG) interpretation. In health economic terms, "more than 3,000 quality-adjusted life-years are lost annually to cerebral palsy from obstetric complications resulting in fetal hypoxia, at estimated costs of £62.9m" (Annual report of the Chief Medical Officer: the health of the 51%: Women, Dept. of Health, London, 2015). Reducing the rate of stillbirths, neonatal deaths and brain injuries that are caused during or soon after birth continues to be a priority for the NHS and it is one of the mandate objectives from central government (The Government's mandate to NHS England 2016-2017, Dept. of Health, 2017).
At the same time, the UK government has pledged to "ensure the benefits of NHS data and innovation are fully harnessed for patients in this country", and with this goal, has recently pledged £250 million for the NHS to invest in Artificial Intelligence (https://www.gov.uk/government/news/health-secretary-announces-250-million-investment-in-artificial-intelligence).
Our proposal fits right into the heart of the above national agendas, and is based on decades of experience of the team with machine learning and working with the CTG archives. Moreover, this proposal fits perfectly within the EPSRC Healthcare Technologies strategy, addressing one of the Grand Challenges: Optimising Treatment through effective diagnosis, patient-specific prediction and evidence-based intervention. In addition, this proposal also fits within the Cross-cutting research capabilities: it is focussed on innovative intelligent technologies that can have a transformative impact on diagnosis and monitoring in healthcare.
This collaborative EPSRC proposal is a crucial building block within our larger programme at the Oxford Centre for Fetal Monitoring Technologies (please see Case for Support - Fig.1 and Pathways to Impact part of this proposal). Our research project is designed to deliver novel advanced deep learning methods for CTG analysis and novel technical capability to interact with the clinicians through an App, supporting the wider project in preparation for a large i4i NIHR Product Development Award. Our overarching goal is to deliver to the bedside, for the first time, a disruptive data-driven individualised decision support tool for fetal health risk assessment during childbirth.
Finally, our proposal is highly innovative without an equivalent world-wide to date, both owing to the unique size and content of the Oxford dataset and to the applicants' expertise and prior work. The timing is perfect with current developments in deep learning methods, existing data archive, strong preliminary results, team's expertise, established collaborations and partnerships, and documented success with attracting funding from the NIHR. Our research programme holds clear potential benefits for the individuals, society, clinicians, and the NHS. Given the existing database, substantial prior work, enormity of NHS litigation claims, costs of unnecessary operative deliveries, and the quality-adjusted life-years lost to intrapartum hypoxic brain injury, this project represents excellent value for money.