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Single brain scan could diagnose Alzheimer’s disease

Overview: A neuroimaging-based machine learning algorithm can detect Alzheimer’s disease in the brain with 98% accuracy. The system is also 79% accurate in determining what stage of Alzheimer’s a patient has.

Source: Imperial College London

The study uses machine learning technology to look at structural features in the brain, including regions not previously associated with Alzheimer’s disease. 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.

Although there is no cure for Alzheimer’s disease, early diagnosis helps patients. It allows them to access help and support, receive 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 cause the disease and support the development and trials of new treatments.

The research was published in Communication 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 this age can also get it. The most common symptoms of dementia are memory loss and problems 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 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, made on a standard 1.5 Tesla machine, commonly found in most hospitals.

The researchers adapted an algorithm developed for use in classifying cancer 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 functions 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 early- and late-stage Alzheimer’s patients, healthy controls, and patients with other neurological disorders, 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 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 stages of 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 method can predict Alzheimer’s disease with this level of accuracy, so our research is an important step forward. Many patients presenting with Alzheimer’s in memory clinics also have other neurological disorders, but even within this group, our system was able to distinguish patients with Alzheimer’s from those who did not.

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This shows the brain with the different areas mapped
This shows a drawing of a brain over a hand
Although most people with Alzheimer’s disease develop it after age 65, people under this age can also get it. Image is in the public domain

“Waiting for a diagnosis can be a terrible experience for patients and their families. If we can shorten the time they have to wait, make diagnosis easier, and reduce some of the uncertainty, it would help immensely. Our new approach could also identify early stage patients for clinical trials of new drug treatments or lifestyle changes, which is currently very difficult to do.”

The new system saw changes in parts 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, vision and the hearing). This opens potential new avenues for research into these areas and their association with Alzheimer’s disease.

dr. Paresh Malhotra, consultant neurologist at Imperial College Healthcare NHS Trust and researcher at Imperial’s Department of Brain Sciences, said: “While neuroradiologists already interpret MRI scans to help diagnose Alzheimer’s, there are likely features of the scans that are not visible, not even for specialists.

“Using an algorithm capable of selecting texture and subtle structural features in the brain that are affected by Alzheimer’s disease could really improve the information we can obtain from standard imaging techniques.”

About this machine learning and research news about Alzheimer’s disease

Author: Maxine Myers
Source: Imperial College London
Contact: Maxine Myers – Imperial College London
Image: The image is in the public domain

Original research: The findings appear in communication medicine

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