Every year, over 200 million fetal heart rate tests are performed worldwide, yet doctors routinely disagree on what the readouts mean. The test—called CTG or electronic fetal monitoring—uses probes on the mother’s abdomen to measure the baby’s heart rate. Clinicians currently interpret these complex signals by eye, a method so unreliable that the same doctor can read the same trace differently on different days. This inconsistency leads to some babies being delivered too early and others too late, contributing to stillbirths—more than 80% of which occur before labour begins. Existing computerised systems reduce disagreement but remain crude: they ignore gestational age, treat a 28-week fetus the same as a 38-week one, and cannot account for maternal or fetal disease. This team will develop artificial intelligence models that learn from a unique database of over 165,000 CTG signals linked to outcomes from more than 56,000 pregnancies. The AI will incorporate gestational age, disease status, and other factors to give clinicians a precise, personalised risk assessment for each fetus. If successful, the tool could transform antenatal care by providing consistent, accurate predictions of fetal wellbeing—reducing unnecessary early deliveries and preventing avoidable stillbirths. The system is designed to work on any CTG machine from any manufacturer, making it scalable across the NHS and globally.
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Electronic Fetal Heart monitoring (also called CTG) is the measurement of the fetal heart rate using probes that are placed on the mother's abdomen. It is the commonest test of fetal wellbeing worldwide (>200M tests per year are performed). It is used to try and assess how healthy the baby is and produces a readout that is very complex; surprisingly this readout is usually analyzed visually (by eye) and the clinician performing this analysis will use this to justify whether a baby needs to be delivered or not. There is a substantial amount of published data that shows this visual assessment is extremely poor and groups of clinicians disagree about a CTG and even the same doctor can interpret a CTG differently on different days. This means that some babies are delivered too soon and many sick babies are delivered too late - both create major problems for the babies, their parents, the NHS and society. CTG can be performed in pregnancy before labour or during labour. Most stillbirths occur before labour (>80%) and we have focused on this area. Many groups (including ourselves) have tried to standardize assessment of the CTG readouts using rudimentary computerised assessment. These systems certainly reduce the disagreements between clinicians but can't account for the multiple factors that make a CTG normal or abnormal (e.g. no systems account for the gestational age of a baby - e.g. treating a 28 week baby the same as a 38 week baby. These systems don't incorporate maternal or fetal disease into the analysis and none of these systems can tell clinicians what will happen to the baby in the coming days or weeks. We have brought together a team with expertise in CTG and artificial intelligence to deliver next generation assessment of the fetus. We will develop a suite of artificial intelligence based machine-learning models to revolutionise antepartum CTG analysis. Recent advances in deep-neural-networks (DNN) enable advanced analysis and identification of novel features within these complex signal patterns which we can exploit in conjunction with detailed maternal and fetal clinical outcomes to generate high fidelity diagnostic and prognostic tools. At our disposal is a unique unrivaled database of >165,000 fully classified CTG signals with associated maternal and neonatal outcome data from >56,000 pregnancies. Leveraging these data, we will develop artificial intelligence based technologies specific to the unique context of the mother and the fetus (at any gestational age). We have proven experience of analysing large datasets using these AI based tools. We have built into our plans the ability to validate our findings with prospective data from Oxford and Melbourne. The potential health benefits are substantial. Our work streams will allow us to generate clean data, generate tools that will allow our AI solution to be used on any CTG from any manufacturer. We will incorporate gestational age, maternal and fetal disease status and provide clinicians with a precise risk assessment of the fetus that will significantly improve the way we care for babies in the UK and beyond.
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