Machine learning techniques for detection of fetal hypoxia and prevention of birth injury

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Lack of oxygen supply in the womb (also known as fetal hypoxia) during labour causes adverse effects on newborns' health, such as metabolic acidosis, neurodevelopmental issues or death. Therefore, fetal monitoring during labour is crucial to prevent the devastating impact of fetal hypoxia on babies and families.Cardiotocography (CTG) is used to monitor fetal heart rate (FHR), which can detect fetal hypoxia in the womb during labour is used up to 90% during labour. Therefore, CTG can help identify compromised fetal (through heart rate variability, acceleration, deceleration and baseline) and categorise this into normal, suspicious and pathological. This allows clinicians to intervene in the labour process such as caesarean sections to reduce the adverse effect on newborns while ensuring the mother's safety. However, visual CTG suffers from interpretation inconsistencies between observers which can delay appropriate intervention, causing harm to babies. Furthermore, some decision making can be intuitive and with some level of uncertainty which may contribute towards discrepancy in CTG interpretation. In addition, visual CTG is vulnerable to misidentifications of hypoxic fetus. False-negative cases cause damaging effects on babies and families, while false-positive cases lead to unnecessary interventions. A systematic review demonstrated a significant increase in caesarean section with CTG monitoring. This increases healthcare costs by increasing hospital admissions length and imposes a risk for the mother, such as infection and injuries towards babies.To tackle the shortcomings of visual CTG, computerised CTG was introduced to aid in decision making for abnormal FHR by standardising interpretations and reducing 'grey-zone' features. This will allow a quicker response to compromised fetal. A randomised control trial had shown that computerised CTG improved the quality of interpretations while minimising decision-making time. However, a meta-analysis of six studies showed no significant improvement in fetal well-being between visual and computerised CTG. A recent randomised control trial, the INFANT trial, investigated the ability of computerised CTG as a support decision tool to reduce poor new-borns outcomes demonstrated no significant difference between visual and computerised CTG and failure of computerised CTG to detect abnormal FHR accurately. From here, there is no evidence that computerised CTG improves outcomes. Hence, researchers had explored implementing machine learning (ML) for categorising FHR. ML is a robust tool in artificial intelligence that has received increasing attention within medical research due to its excellent decision-making outcomes. ML can learn and identify patterns from previous data to gain information and experience to make predictions on new data. While ML techniques showed promising results, but studies have so far been limited. For example, studies have used restricted numbers of 'cases' and 'controls' from specific clinical trial conditions, have been dependent on arbitrarily selected heart rate features, and/or used surrogate outcomes such as expert classification of normal/abnormal CTG. Rigorous development and validation of prediction models using large-scale real-world data and clinically significant neonatal outcomes are required before methods could be translated to clinical practice. Therefore, this project aims to develop and validate machine learning algorithms to predict fetal hypoxia using CTG analysis and clinical risk factors obtained from electronic health records.This collaborative project with the medical software company Clevermed offers new opportunities to analyse EFM alongside detailed clinical data. Clevermed produces the BadgerNet Platform used for neonatal and maternity records, including storage of EFM traces. The system is used in 26 Maternity units in the UK and Australia and New Zealand, capturing over 150,000 pregnancies and newborns per annum.