A single brain scan can diagnose Alzheimer’s disease

The research uses machine learning technology to examine structural features of the brain, including in regions not previously associated with Alzheimer’s disease. The advantage of the technique is its simplicity and the fact that it allows the disease to be identified at an early stage when it can be very difficult to diagnose.

Although there is no cure for Alzheimer’s disease, prompt diagnosis at an early stage helps patients. 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 at an early stage of the disease will also help researchers understand the brain changes that trigger the disease and support the development and testing of new treatments.

The research is published in the Nature Portfolio Journal, Communications Medicine, and funded by the Imperial Biomedical Research Center of the National Institute for Health and Care Research (NIHR).

Alzheimer’s disease is the most common form of dementia, affecting over half a million people in the UK. Although most people with Alzheimer’s disease develop it after age 65, people under that age can also develop it. The most common symptoms of dementia are memory loss and difficulty with thinking, problem solving and language.

Doctors currently use a range 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 for organization and processing.

The new approach only requires one of them – a magnetic resonance imaging (MRI) brain scan performed on a standard 1.5 Tesla machine, commonly found in most hospitals.

The researchers adapted an algorithm developed for use in classifying cancerous tumors and applied it to the brain. They divided the brain into 115 regions and assigned 660 different characteristics, such as size, shape and texture, to assess each region. They then trained the algorithm to identify where changes in these characteristics could accurately predict the existence of Alzheimer’s disease.

Using data from the Alzheimer’s Disease 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. suffering from other neurological conditions, including frontotemporal dementia and Parkinson’s disease. They also tested it with data from more than 80 patients undergoing diagnostic testing for Alzheimer’s disease at Imperial College Healthcare NHS Trust.

They found that in 98% of cases, the MRI-based machine learning system alone could accurately predict whether or not the patient had Alzheimer’s disease. It was also able to distinguish between early and advanced stages of Alzheimer’s disease with fairly high accuracy, in 79% 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 method 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 disease to memory clinics also have other neurological disorders, but even within this group our system could distinguish patients with Alzheimer’s disease from those with didn’t have any.

“Waiting for a diagnosis can be a horrific experience for patients and their families. If we could reduce the wait time, simplify the diagnostic process and reduce some of the uncertainty, that would help a lot. Our new approach could also identify patients at an early stage for clinical trials of new drug treatments or lifestyle changes, which is currently very difficult to do.

The new system spotted changes in areas of the brain that were not associated with Alzheimer’s disease, including the cerebellum (the part of the brain that coordinates and regulates physical activity) and the ventral diencephalon (related to the senses , sight and hearing). This opens up potential new avenues of research in these areas and their links to Alzheimer’s disease.

Dr Paresh Malhotra, consultant neurologist at Imperial College Healthcare NHS Trust and researcher in Imperial’s Department of Brain Sciences, said: “While neuroradiologists already interpret MRI scans to help diagnose Alzheimer’s disease, it is likely that the scans show features that are not visible even to specialists. Using an algorithm that can pick out the texture and subtle structural features of the brain that are affected by Alzheimer’s disease could really improve the insights we can get from standard imaging techniques.

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