Artificial Intelligence Could Help Catch Alzheimer's Early

Artificial Intelligence Could Help Catch Alzheimer’s Early

Artificial Intelligence Could Help Catch Alzheimer's Early
The devastating neurodegenerative condition Alzheimer’s disease is incurable, but with early detection, patients can seek treatments to slow the disease’s progression, before some major symptoms appear.  Now, by applying artificial intelligence algorithms to MRI brain scans, researchers have developed a way to automatically distinguish between patients with Alzheimer’s and two early forms of dementia that can be precursors to the memory-robbing disease.

The researchers, from the VU University Medical Center in Amsterdam, suggest the approach could eventually allow automated screening and assisted diagnosis of various forms of dementia, particularly in centers that lack experienced neuroradiologists.

Additionally, the results, published online July 6 in the journal Radiology, show that the new system was able to classify the form of dementia that patients were suffering from, using previously unseen scans, with up to 90 percent accuracy. [10 Things You Didn’t Know About the Brain]

“The potential is the possibility of screening with these techniques so people at risk can be intercepted before the disease becomes apparent,” said Alle Meije Wink, a senior investigator in the center’s radiology and nuclear medicine department.

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“I think very few patients at the moment will trust an outcome predicted by a machine,” Wink told Live Science. “What I envisage is a doctor getting a new scan, and as it is loaded, software would be able to say with a certain amount of confidence [that] this is going to be an Alzheimer’s patient or [someone with] another form of dementia.”

Similar machine-learning techniques have already been used to detect Alzheimer’s disease; in those implementations, the techniques were used on structural MRI scans of the brain that can show tissue loss associated with the disease.

But scientists have long known that the brain undergoes functional changes before these structural changes kick in, Wink said. Positron emission tomography (PET) imaging has been a popular method for tracking functional changes, but it is invasive and expensive, he added.

Instead, Wink and his colleagues used an MRI technique called arterial spin labeling (ASL), which measures perfusion — the process of blood being absorbed into a tissue — across the brain. The method is still experimental, but it is noninvasive and applicable on modern MRI scanners.

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Previous studies have shown that people with Alzheimer’s typically display decreased perfusion (or hypoperfusion) in brain tissue, which results in insufficient supply of oxygen and nutrients to the brain.

Using so-called perfusion maps from patients at the medical center, Wink’s team trained its system to distinguish among patients who had Alzheimer’s, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).

The brain scans of half of the 260 participants were used to train the system, and the other half were then used to test if the system could distinguish among different conditions when looking at previously unseen MRI scans.

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