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Testing an innovative cot-side brain scanner to identify newborn babies most at risk of lifelong disability from brain injury

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Around 55,000 babies are born too soon each year in the UK, with around 1,500 diagnosed with brain injury.[1-3] In addition, around 2,200 full-term newborn babies experience brain injury at or around birth.[1,3] Identifying babies who are at greatest risk of lifelong disability from brain injury remains a major challenge. Professor Topun Austin at the University of Cambridge is testing an innovative cot-side scanning device that provides an unprecedented window into how a baby’s brain is working. This new tool could ultimately transform the diagnosis and treatment of brain injuries, improving long-term outcomes for sick and preterm babies worldwide.

How are children’s lives affected now?

Brain injury can occur in the very early days of life, often due to complications around the time of birth that disrupt the supply of blood and oxygen to the brain. Thanks to major advances in neonatal care, more babies are surviving than ever before – but they remain at increased risk of long-term neurodevelopmental complications.

“Brain injury can cause lifelong disabilities such as cerebral palsy, epilepsy, behavioural challenges and learning difficulties,” says Professor Austin. “These life-changing consequences can have a profound impact on a child’s health and wellbeing and can place a huge burden on families.”

Identifying sick and preterm babies who are most at risk of lifelong disability from brain injury would allow doctors to provide earlier, more targeted care to help reduce the impact on children’s lives. However, while standard brain imaging is good at diagnosing brain injury, it is not as good at predicting which babies will have long-term problems.

“A reliable, easy-to-use and highly accurate diagnostic tool is urgently needed to improve our prediction of long-term neurodevelopmental outcomes, and personalise treatment,” says Professor Austin.

How could this research help?

“We’re testing an innovative brain scanning device to see if it can improve how doctors can predict the outcomes of sick and preterm babies with brain injuries, while they sleep comfortably in their cots,” says Professor Austin. 

This non-invasive, portable device combines two advanced imaging tools that provide clear, real-time pictures of how a baby’s brain is working – rather than just what it looks like – which could help provide doctors with a better understanding of which babies are most at risk of developing long-term problems.

“We will first test the device in healthy preterm and full-term babies, gathering feedback from parents and healthcare staff to ensure it is practical and easy to use,” says Professor Austin. “Next, we will monitor preterm babies over time to track how their brain activity changes as they grow, providing valuable insights into early brain development.”

The team will then study newborn babies with brain injuries to understand how their brain activity differs from healthy babies – providing crucial insights to inform future predictive models linking early brain function to later outcomes. 

This system could help transform neonatal care by enabling early diagnosis and more targeted treatment for brain injuries – ultimately improving long-term outcomes for children worldwide.

Professor Austin

References

  1. Office for National Statistics: Vital statistics in the UK: births, deaths and marriages -February 2023. [website accessed 22 December 2025]
  2. Office for National Statistics: Birth Characteristics in England and Wales: 2022. [website accessed 22 December 2025]
  3. Gale, C. et al. Neonatal brain injuries in England: population-based incidence derived from routinely recorded clinical data held in the National Neonatal Research Database. Arch Dis Child Fetal Neonatal Ed. 2018 103(4):F301-F306. 

Research table

Project details

Project Leader Professor Topun Austin MBBS, MRCP, MRCPCH, PhD
Location University of Cambridge
Project Team Dr Robert Cooper PhD
Dr Charlie Demene PhD, HDR
Andrea Edwards MSc
Other Locations Cambridge University Hospital NHS Foundation Trust
University College London
Grant Amount £114,974
Duration 24 months
Grant Code (GN number) GN4030

 

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