Britain has developed a new technology that helps diagnose Alzheimer's quickly and accurately21 June, 2022
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British researchers have come up with a new technology that helps diagnose Alzheimer's disease quickly and accurately, and the new research breakthrough uses machine learning technology to look at features within the brain, including areas that were not previously associated with Alzheimer's disease, according to scitechdaily.
The advantage of this technique is its simplicity and the fact that it is able to recognize the disease at an early stage when it is very difficult to diagnose.
Although there is no cure for Alzheimer's disease, getting a prompt diagnosis at an early stage helps patients, as it allows them to access help and support, get treatment to manage their symptoms and plan for the future. Being able to accurately identify patients early in the disease will also help researchers understand the brain changes that cause disease, and support the development and trial of new treatments.
Alzheimer's disease is the most common form of dementia, and although most people with Alzheimer's develop it after the age of 65, people younger than this age can also develop it.
The most common symptoms of dementia are memory loss and difficulty thinking, problem solving and language.
According to the study, published in the Nature Portfolio Journal, and funded by the National Institute for Health and Care Research (NIHR), the Imperial Center for Biomedical Research in Britain, doctors are currently using a wide range of tests to diagnose Alzheimer's disease, including memory and cognitive tests and a brain scan. Scans are used to check for protein deposits in the brain and shrinkage of the hippocampus, the area of the brain associated with memory. All of these tests can take several weeks to arrange and process.
The new approach requires only one of those — a brain scan with magnetic resonance imaging (MRI) taken on a standard 1.5 Tesla machine, which is commonly found in most hospitals.
Researchers have adapted an algorithm developed for use in grading cancerous tumors and applied it to the brain. They divided the brain into 115 regions and assigned 660 different traits, such as size, shape and texture, to assess each region and then trained the algorithm to identify where changes in these features could accurately predict the presence of Alzheimer's disease. Using data from the Alzheimer's Neuroimaging Initiative, the team tested their approach on brain scans of more than 400 patients with early and late-stage Alzheimer's disease, healthy controls and patients with other neurological conditions, including frontotemporal dementia and Parkinson's disease.
They also tested it using data from more than 80 patients undergoing diagnostic tests for Alzheimer's disease at Imperial College Hospitals.
They found that in 98 percent of cases, an MRI-based machine learning system could accurately predict whether a patient had Alzheimer's disease.
It was also able to distinguish between early and late-stage Alzheimer's disease with fairly high accuracy, in 79 percent of patients.
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