Advantages of this new approach include its ease of use and the fact that it can detect Alzheimer’s disease at an early stage when it is often hardest to diagnose.
The study employs machine learning to examine structural brain traits, even in regions not previously related to Alzheimer’s. Advantages of this approach include its ease of use and the fact that it can detect the disease at an early stage when it is often hardest to diagnose.
Although there is currently no treatment for Alzheimer’s disease, early detection is beneficial. It enables patients to seek assistance and support, as well as receive therapy to control their symptoms and make plans for the future. Accurately identifying individuals at an early stage of the disease will also aid in the study of the brain alterations that set off the disease and the testing of potential treatments.
The research was funded by the National Institute of Health and Care Research (NIHR) Imperial Biomedical Research Centre and published in the Nature Portfolio Journal, Communications Medicine.
Millions of people in the world are living with Alzheimer’s disease, making it the most prevalent form of dementia. Alzheimer’s disease strikes the majority of people once they reach the age of 65, but it can strike anyone at any age. Memory loss and difficulties with thinking, problem solving, and language are the most common dementia symptoms.
Memory and cognitive tests, as well as brain scans, are just some of the tools currently available to doctors for diagnosing Alzheimer’s disease. The scans are used to look for protein deposits in the brain as well as atrophy of the hippocampus, the memory-related part of the brain. Getting all of these tests set up and done can take a few weeks.
Only one of them is required in the new method: a magnetic resonance imaging (MRI) brain scan performed on a typical 1.5 Tesla equipment, which is widely available in hospitals.
The researchers took an algorithm that was made to classify cancer tumors and used it to study the brain. To examine each region, they divided the brain into 115 regions and assigned 660 distinct parameters, such as size, shape, and texture. Then, they taught the algorithm where changes in these features could accurately predict whether or not a person had Alzheimer’s disease.
Using data from the Alzheimer’s Disease Neuroimaging Initiative, the team tested their method on brain scans from more than 400 people with early and late stages of Alzheimer’s, healthy controls, and people with other neurological conditions like frontotemporal dementia and Parkinson’s disease. They also used information from more than 80 people with Alzheimer’s who were getting tests at Imperial College Healthcare NHS Trust.
They discovered that the MRI-based machine learning system could accurately determine whether a patient had Alzheimer’s disease or not in 98 percent of cases. In 79 percent of patients, it was also able to identify between early and late-stage Alzheimer’s disease with a high degree of accuracy.
“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,” said Professor Eric Aboagye of Imperial College’s Department of Surgery and Cancer, who led the research.
“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 .”
For patients and their families, waiting for a diagnosis can be a nightmare. It would be really beneficial if we could reduce the amount of time patients have to wait, make diagnosis an easier procedure, and remove some of the ambiguity.
“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,” said the authors.
The new approach detected abnormalities in the cerebellum (the portion of the brain that organizes and governs physical activity) and the ventral diencephalon, which were previously unrelated to Alzheimer’s disease (linked to the senses, sight and hearing). This could pave the way for further investigation into the possible connections between these factors and Alzheimer’s disease.
“Although neuroradiologists already interpret MRI scans to help diagnose Alzheimer’s, there are likely to be features of the scans that aren’t visible, even to specialists,” added Dr. Paresh Malhotra, a consultant neurologist at Imperial College Healthcare NHS Trust and a researcher in Imperial’s Department of Brain Sciences.
“Using an algorithm able to select texture and subtle structural features in the brain that are affected by Alzheimer’s could really enhance the information we can gain from standard imaging techniques.”
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