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Imaging and AI reveal rehab potential after brain injury

Study reveals high levels of accuracy when testing among patients in intensive care

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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.

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