Published: 20 June 2022
A single MRI scan of the brain could be enough to diagnose Alzheimer’s disease, according to new research supported by NIHR.
Researchers developed an algorithm to analyse structural features shown on brain MRI scans, including in regions not previously associated with Alzheimer’s. This machine learning technology was able to accurately predict the existence of Alzheimer’s disease and identify the disease at an early stage, when it can be very difficult to diagnose.
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 the age of 65, people under this age can develop it too. The most frequent symptoms of dementia are memory loss and difficulties with thinking, problem solving and language.
Currently lots of tests are used 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 to arrange and to process.
Getting a diagnosis quickly at an early stage helps patients 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 to understand the brain changes that trigger Alzheimer’s disease, and support development and trials of new treatments.
The researchers, supported by Imperial Biomedical Research Centre, studied just one of the tests currently used to diagnose Alzheimer’s disease - an MRI scan. They adapted an algorithm developed for use in classifying cancer tumours and applied it to MRI scans of the brain.
The researchers divided the brain into 115 regions and allocated 660 different features, such as size, shape and texture. They then trained the algorithm to identify where changes to these features 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 from over 400 patients with early and later stage Alzheimer’s, healthy controls and patients with other neurological conditions, including frontotemporal dementia and Parkinson’s disease. They also tested it with data from more than 80 patients undergoing diagnostic tests for Alzheimer’s at Imperial College Healthcare NHS Trust.
The research, published in the Nature Portfolio Journal Communications Medicine, found that in 98% 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-stage Alzheimer’s with fairly high accuracy, in 79% of patients.
The new system spotted 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 ventral diencephalon (linked to the senses, sight and hearing). This opens up potential new avenues for research into these areas and their links to Alzheimer’s disease.
Professor Eric Aboagye, from Imperial’s Department of Surgery and Cancer, who led the research, said: “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. 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.
“Waiting for a diagnosis can be a horrible experience for patients and their families. If we could cut down the amount of time they have to wait, make diagnosis a simpler process, and reduce some of the uncertainty, that would help a great deal. 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.”
Dr Paresh Malhotra, who is a consultant neurologist at Imperial College Healthcare NHS Trust and a researcher in Imperial’s Department of Brain Sciences, said: “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. 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.”