Alzheimer’s disease: a single fluorescence imaging for its diagnosis

Although there is no cure for Alzheimer’s disease, the ability to make an early diagnosis can greatly help patients, by enabling them to access professional help and support, and treatment to manage symptoms, as well as enabling them to plan ahead. The ability to accurately identify patients early in the disease also allows research teams to better identify brain changes early in the disease. Although most people with Alzheimer’s develop the disease after age 65, the disease is more and more frequently diagnosed in young adults, with early interventions significantly slowing its progression.

One brain scan is enough

Doctors currently use a range of tests to diagnose Alzheimer’s disease, including memory tests, cognitive tests, and brain scans. The scans are used to assess the presence of protein deposits in the brain and shrinkage of the hippocampus, the area of ​​the brain associated with memory. All of these tests may take several weeks.

new approach It requires only one brain scan with magnetic resonance imaging (MRI) performed on a standard 1.5 Tesla machine, commonly found in most hospitals. The researchers developed an algorithm based on

660 different properties of 115 brain regions.

Among this data is the size, shape, and texture of each area. The algorithm can identify abnormalities in these characteristics, which makes it possible to accurately predict the presence of Alzheimer’s disease. Tested 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, the approach demonstrates its detection ability and accuracy. The new system can also detect 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 diencephalon (related to the senses, sight and hearing) — opening new avenues for research into the etiology of Alzheimer’s disease.

then test From data from more than 80 patients undergoing diagnostic tests for Alzheimer’s disease at Imperial College Healthcare, the diagnosis confirms its efficacy:

  • In 98% of cases, a machine learning system based on MRI data allows it to single-handedly and accurately predict whether a patient has Alzheimer’s disease or not;
  • Moreover, the system makes it possible to distinguish the early and advanced stages of Alzheimer’s disease with a fairly high accuracy, in 79% of patients.

Lead author Professor Eric Aboage, from the Department of Surgery and Cancer at Imperial, comments on these findings: Currently, there is no other simple and widely available method that can predict Alzheimer’s disease with this level of accuracy. So it is an important step forward. Many patients with Alzheimer’s disease in memory clinics also have other neurological disorders, but even within this group, our system can distinguish Alzheimer’s patients from other patients.

Finally, this system also makes it possible to shorten the waiting period for diagnosis, which is a traumatic experience for patients and their families.

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