In medical diagnosis news, a new technique has been developed that can diagnose Alzheimer's disease in a single scan. A study on the new method has been fully described in Communications Medicine, a Nature Portfolio Journal.
According to the study, the new technique examines anatomical aspects of the brain, including those not previously linked to Alzheimer's disease, using machine learning technologies. The technique's benefit is its simplicity and the fact that it can detect the condition early on when it can be challenging to diagnose.
Although there is currently no cure for Alzheimer's disease, sufferers can and do benefit from receiving a diagnosis as soon as possible. Proper and timely diagnosis enables patients to seek assistance and support, as well as receive therapy to control their symptoms and plan for the future. The ability to correctly identify patients at an early stage of the disease will aid researchers in better understanding the brain alterations that cause the disease and developing and testing new treatments.
Alzheimer's disease is the most common type of dementia, affecting more than 500,000 people in the United Kingdom alone. 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.
Alzheimer's disease is currently diagnosed using a variety of tests, including memory and cognitive exams, as well as brain scans.
How does the new Alzeihemer diagnosis work?
The new scans are used to look for protein deposits in the brain and atrophy of the hippocampus, the memory-related part of the brain. The preparation and processing of all of these tests can take several weeks.
The new method makes use of a typical 1.5 Tesla magnetic resonance imaging (MRI) brain scan, which can be found at most hospitals.
The researchers created a classification algorithm for cancer tumors and applied it to the brain. To examine each region, they divided the brain into 115 regions and assigned 660 distinct parameters, such as size, shape, and texture. The computer was then taught to spot changes in these traits that might accurately indicate the presence of Alzheimer's disease.
The team tested their method on brain scans from over 400 patients with early and later-stage Alzheimer's disease, healthy controls, and patients with other neurological conditions such as frontotemporal dementia and Parkinson's disease, using data from Alzheimer's Disease Neuroimaging Initiative. They also put it to the test with data from over 80 Imperial College Healthcare NHS Trust patients who had Alzheimer's diagnostic tests.
They discovered that the MRI-based machine learning system could effectively 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.
Professor Eric Aboagye, from Imperial's Department of Surgery and Cancer, who led the research, explained in an interview that "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," he added.
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 brings up new study opportunities in these regions and their connections to Alzheimer's disease.
Dr. Paresh Malhotra, who is a consultant neurologist at Imperial College Healthcare NHS Trust and a researcher in Imperial's Department of Brain Sciences, added that "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 the 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."
Plain language study summary:
"Alzheimer’s disease is the most common cause of dementia, impacting memory, thinking, and behavior. It can be challenging to diagnose Alzheimer’s disease which can lead to suboptimal patient care. During the development of Alzheimer’s disease, the brain shrinks and the cells within it die. One method that can be used to assess brain function is magnetic resonance imaging, which uses magnetic fields and radio waves to produce images of the brain. In this study, we develop a method that uses magnetic resonance imaging data to identify differences in the brain between people with and without Alzheimer’s disease, including before obvious shrinkage of the brain occurs. This method could be used to help diagnose patients with Alzheimer’s Disease."