A groundbreaking new method involving imaging and machine learning can help identify rehab potential after brain injury.
Combining functional magnetic resonance imaging (fMRI) with state-of-the art machine learning techniques, the technique can be used in intensive care unit (ICU) patients to help predict survival chances and the likelihood of recovery.
And initial tests have revealed this can be measured with an accuracy of 80 per cent.
The impact of brain injury can vary significantly, and its consequences can take months or even years to fully manifest, which creates great uncertainty for medical teams and families.
An interdisciplinary team of researchers from Western University, in collaboration with neurologists at London Health Sciences Centre and Lawson Health Research Institute sought to find a solution to this problem.
They were led by Dr Loretta Norton, who was one of the first researchers in the world to measure brain activity in the ICU.
The team measured brain activity in 25 patients at one of London’s two ICUs in the first few days after a serious brain injury, and tested whether it could predict who would survive and who would not.
“We previously found that information about the potential for recovery in these patients was captured in the way different brain regions communicate with each other,” said Dr Norton.
“Intact communication between brain regions is an important factor for regaining consciousness.”
The breakthrough occurred when the team realised they could combine this imaging technique with an application of Artificial Intelligence (AI) known as machine learning.
They found they could predict patients who would recover with an accuracy of 80 per cent, which is higher than the current standard of care.
“Modern artificial intelligence has shown incredible predictive capabilities. Combining this with our existing imaging techniques was enough to better predict who will recover from their injuries,” said graduate Matthew Kolisnyk, who is part of the team.
While encouraging, the researchers say the prediction was not perfect and needs further research and testing.
“Given that these models learn best when they have lots of data, we hope our findings will lead to further collaborations with ICUs across Canada,” said graduate Karnig Kazazian.
- Legal4 weeks ago
APIL warns increasing court fees may price out injured claimants
- Community neuro rehab2 weeks ago
Cambridgeshire rehab centre sets new standard for green energy use and sustainability
- Stroke news3 weeks ago
‘It got me focused on moving forward’ Alex’s recovery from a stroke
- News1 week ago
Your NR Headlines: Thursday 15th February
- News3 weeks ago
Your NR Headlines: Wednesday 7th February
- Uncategorised5 days ago
Our brains learn better from people we like
- Brain injury news4 weeks ago
Children with mTBIs at increased risk of affective and behavioural disorders – study
- Brain injury news2 weeks ago
Patients and professionals discuss benefits of canoeing and kayaking on brain injury rehab