The chronic neurological condition called Alzheimer’s disease is one of the more insidious in modern society. In 2015, some 30 million were thought to have this disease. As a hugely expensive condition to manage, this places significant burdens on health-care systems all over the world.
Although there is no known way to halt the disease in advanced cases, there is evidence that its progression can be slowed or halted if it is identified early. So finding a reliable way to spot people who are at risk of developing the disease is an important goal.
Today, Hongyoon Choi at the Cheonan Public Health Center and Kyong Hwan Jin at the Korea Advanced Institute of Science and Technology, both in South Korea, say they have used deep learning to develop just such a technique. These guys say their process can accurately identify people likely to develop Alzheimer’s in the next three years.
Cognitive decline is inevitable as we age. We tend to become more forgetful, lose our train of thought more often, and find it harder to make decisions or accomplish tasks. Doctors call this mild cognitive impairment, and it affects most people as they get older.
Many people with mild cognitive impairment go on to develop Alzheimer’s, which is much more severe. People with this condition lose their vocabulary, frequently using incorrect word substitutions. They stop recognizing close relatives, lose basic self-care skills and eventually become entirely dependent on caregivers. Most die within a few years of diagnosis.
But curiously, not all people with mild cognitive impairment follow this path. Some never deteriorate and a few even improve. So doctors would dearly love to be able to spot those likely to develop Alzheimer’s because they are most likely to benefit from treatment.
One way to do this is by studying positron emission tomography (PET) scans of the brain. Alzheimer’s is known to be characterized by the unwanted growth of protein clumps called amyloid plaques and by a slow brain metabolism as measured by the rate at brain uses glucose.
Certain types of PET scans can reveal signs of both these conditions and can therefore be used to spot people with mild cognitive impairment who are most at risk of developing Alzheimer’s.
That’s the theory. In practice, interpreting the images is hard. Researchers have found one or two strong markers that trained observers can look for, but this method is time-consuming and prone to error.
Enter Hongyoon and Kyong who have replaced human observers in this process with a deep-learning neural network.
Their method is straightforward. In recent years, Alzheimer’s researchers around the world have been constructing a database of brain images of people with and without Alzheimer’s. Hongyoon and Kyong use this database to train a convolutional neural network to recognize the…