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Details of Grant 

EPSRC Reference: EP/V002511/1
Title: Deep Learning Models for Fetal Monitoring and Decision Support in Labour
Principal Investigator: Jordanov, Dr I
Other Investigators:
Lee, Professor RR Georgieva, Dr AE Namburete, Dr AIL
Researcher Co-Investigators:
Project Partners:
Oxford Uni. Hosps. NHS Foundation Trust
Department: Sch of Computing
Organisation: University of Portsmouth
Scheme: Standard Research
Starts: 01 April 2021 Ends: 31 July 2024 Value (£): 609,686
EPSRC Research Topic Classifications:
Artificial Intelligence
EPSRC Industrial Sector Classifications:
Healthcare Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
06 Jul 2020 EPSRC ICT Prioritisation Panel July 2020 Excluded
01 Oct 2020 EPSRC ICT Prioritisation Panel October 2020 Announced
Summary on Grant Application Form
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.

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Organisation Website: http://www.port.ac.uk