Computer Approach with Brain Scan Helps Predict Alzheimer’s Disease

Researchers from the UK have accurately predicted Alzheimer’s disease using a computerised approach coupled with MRI brain scans. The scientific journal Nature Communications Medicine published the findings.

Using MRI brain scans from over 400 study volunteers from the Alzheimer’s Disease Neuroimaging Initiative they tested their approach.

Using a two-step approach, the computer model weighs up whether the participant is likely to have Alzheimer’s disease or not.

They also tested their approach on a smaller number of people who underwent diagnostic tests at Imperial College Healthcare NHS Trust.

They found that in 98 out 100 cases, the approach could accurately predict whether the patient had Alzheimer’s disease or not.

It was also able to distinguish between early and late-stage Alzheimer’s but only in 79 out of 100 participants.

Dr Rosa Sancho, Head of Research at Alzheimer’s Research UK, said:
“Alzheimer’s disease is the most common cause of dementia. We desperately need to see better ways to tackle Alzheimer’s, and this requires progress on a number of fronts. A key part of turning the tide is improving how we identify and diagnose people with the disease. This will enable patients to access support and available treatments, but also help to address some of the significant challenges in recruiting to dementia research, particularly clinical trials.

“Doctors may request an MRI for people with suspected Alzheimer’s, but these scans cannot conclusively show whether or not someone has the disease. At the moment an MRI can help to rule out other potential causes of memory and thinking problems, such as a brain tumor, or to see if there are signs of brain shrinkage that could help distinguish between different diseases that cause dementia.

“In this study, scientists developed a computerised approach to predict if someone has Alzheimer’s disease. The researchers analysed MRI brain scans captured routinely in hospitals and then used a computer algorithm to generate a complex picture of the brain and predict Alzheimer’s.

“This is not the first-time using computer technology like this has shown promise, outperforming scans that look for a single measure of brain health alone. However, the computational effort to process the information from the brain scan is large and future research is needed to understand how the process can be made more efficient.

“Through our own research we know the public support having brain scans to help know their own risk of developing the disease. These findings will need to be further developed before we know how it could benefit people undergoing diagnosis in the clinic. We need to see sustained funding and ambition for dementia research to turn promising discoveries like this into real world breakthroughs that are crucial to improving the diagnostic pathway and preparing the NHS for future treatments.”