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There is currently no cure for brain decline, which makes recieving a dementia diagnosis a death sentence. However, researchers are increasingly identifying dementia at earlier stages. This raises the hope of one day forestalling brain decline. A new study has deployed cutting edge technology to advance this effort.

Researchers have found a more accurate way to predict the development of dementia: by analysing data from a single magnetic resonance imaging (MRI) brain scan using a machine learning algorithm.

The research, published in Nature Portfolio Journal, Communications Medicine, uses machine learning technology to look at structural features within the brain, ofloxacin otic indications including in regions not previously associated with Alzheimer’s.

The advantage of the technique is its simplicity and the fact that it can identify the disease at an early stage when it can be very difficult to diagnose.

Doctors currently use a raft of tests to diagnose Alzheimer’s disease, including memory and cognitive tests and brain scans.

The scans are used to check for protein deposits in the brain and shrinkage of the hippocampus, the area of the brain linked to memory. All of these tests can take several weeks, both to arrange and to process.

The new approach requires just one of these – a magnetic resonance imaging (MRI) brain scan taken on a standard 1.5 Tesla machine, which is commonly found in most hospitals.

The researchers adapted an algorithm developed for use in classifying cancer tumours, and applied it to the brain.

They divided the brain into 115 regions and allocated 660 different features, such as size, shape and texture, to assess each region. They then trained the algorithm to identify where changes to these features could accurately predict the existence of Alzheimer’s disease.

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Using data from the Alzheimer’s Disease Neuroimaging Initiative, the team tested their approach on brain scans from over 400 patients with early and later stage Alzheimer’s, healthy controls and patients with other neurological conditions, including frontotemporal dementia and Parkinson’s disease.

They also tested it with data from over 80 patients undergoing diagnostic tests for Alzheimer’s at Imperial College Healthcare NHS Trust.

They found that in 98 percent of cases, the MRI-based machine learning system alone 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 with fairly high accuracy, in 79 percent of patients.

Professor Eric Aboagye, from Imperial’s Department of Surgery and Cancer, who led the research, said: “Currently no other simple and widely available methods can predict Alzheimer’s disease with this level of accuracy, so our research is an important step forward. Many patients who present with Alzheimer’s at memory clinics do also have other neurological conditions, but even within this group our system could pick out those patients who had Alzheimer’s from those who did not.

“Waiting for a diagnosis can be a horrible experience for patients and their families. If we could cut down the amount of time they have to wait, make diagnosis a simpler process, and reduce some of the uncertainty, that would help a great deal. Our new approach could also identify early-stage patients for clinical trials of new drug treatments or lifestyle changes, which is currently very hard to do.”

The new system spotted changes in areas of the brain not previously associated with Alzheimer’s disease, including the cerebellum (the part of the brain that coordinates and regulates physical activity) and the ventral diencephalon (linked to the senses, sight and hearing).

This opens up potential new avenues for research into these areas and their links to Alzheimer’s disease.

Doctor 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 tumour, 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.”

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