News and Views
Revolutionary UCC research into AI-powered brain screening may save the lives of newborns
- University College Cork researchers discover revolutionary method of analysing newborn babies’ brainwaves.
- AI-assisted approach uses sound, rather than visual images, to analyse brainwaves (EEG monitoring).
- New approach is more accessible to a wider cohort of medical professionals and has the potential to make EEG monitoring more prevalent in medical settings - including those in disadvantaged communities.
- “A potential game-changer in the EEG monitoring industry as the new method brings both accuracy and speed while requiring no training to be adopted in clinical settings."
An interdisciplinary team from University College Cork (UCC) has discovered an ingenious method for analysing neonatal brain waves through artificial intelligence-assisted sonification. The findings have been published in the prestigious international journal Nature Scientific Reports.
The study proposes a revolutionary approach which will empower healthcare professionals to leverage Artificial Intelligence (AI) decision-support using the auditory perception of brainwaves, leading to faster response times and the potential to save many vulnerable newborn babies.
Analysing brainwaves or Electroencephalograms (EEG) is the gold standard in detecting anomalies in brain activity such as seizures. This ground-breaking research has the potential to make EEG monitoring more prevalent in medical settings - including those in disadvantaged communities - leading to faster response times and the potential to save many vulnerable lives. It also reduces the burden of analysing EEG data, allowing two hours of EEG to be screened in just three seconds.
The new method of analysis extends the concept of the stethoscope used by doctors to listen to heart, lung or other sounds from a patient’s body to also listen to brainwaves. While neurophysiologists use EEG recordings to identify seizures visually, this is a slow and cumbersome process for the medic, which involves scrolling through thousands of images. This expertise also requires a significant amount of training that is not readily available on a continuous basis in all hospitals.
This new AI-driven mechanism ingeniously converts brainwaves to sound. Human ears are more sensitive to changes in frequency and evolution of morphology in time – the signature of many seizures. By focusing the listener’s attention to interesting segments in the recording, EEG seizures can be distinctly heard.
Feedback from the end-users, such as medical professionals with no training in interpreting raw neonatal EEG, indicates that the accuracy of detecting the presence of a seizure in a long recording using this method is on par with experienced neurophysiologists trained to visually interpret neonatal EEG.
Professor Andriy Temko, co-supervisor of the study, School of Engineering and Architecture, UCC, commented:
“A lack of interpretation expertise has always been a bottleneck in the widespread usage of EEG monitoring in newborns. To address that, previous research has focused on developing black box AI models to analyse EEG signals. While outstanding performances were obtained, the practical applicability was limited. The AI was uninterpretable by most and useful for those who already know how to analyse neonatal EEG. We have developed a method where AI augments human senses in an explainable manner to keep a healthcare professional in the decision-making loop. It is a potential game-changer in the EEG monitoring industry as the new method brings both accuracy and speed while requiring no training to be adopted in clinical settings."
Dr. Emanuel Popovici, co-supervisor of the study, School of Engineering and Architecture, UCC, added:
“This potentially high-impact research further demonstrates the importance of interdisciplinary research and the power of openly available EEG data. It is another great example of the type of projects which can better humanity. The study opens up many possibilities in the future, from battery-operated edge devices to bringing this technology closer to the patient through commercialisation - ultimately contributing to improved care in disadvantaged communities settings.”