�鶹��ý

Artificial intelligence can identify changes in the brains of people likely to get ��������𾱳����’s disease almost a decade before doctors can diagnose the disease from symptoms alone.

The technique uses non-invasive MRI scans to identify alterations in how regions of the brain are connected.

��������𾱳����’s is a neurodegenerative disease that is the leading cause of dementia for the elderly, eventually leading to loss of memory and cognitive functions.

The race is on to diagnose the disease as early as possible. Although there is no cure, drugs in development are likely to work better the earlier they are given. An early diagnosis can also allow people to start making lifestyle changes to help slow the progression of the disease.

In an effort to enable earlier diagnosis, Nicola Amoroso and at the University of Bari in Italy and their colleagues developed a machine-learning algorithm to discern structural changes in the brain caused by ��������𾱳����’s disease.

First, they trained the algorithm using 67 MRI scans, 38 of which were from people who had ��������𾱳����’s and 29 from healthy controls. The scans came from the at the University of Southern California in Los Angeles.

Positive discrimination

The idea was to teach the algorithm to correctly classify and discriminate between diseased and healthy brains. The researchers divided each brain scan into small regions and analysed the neuronal connectivity between them, without making any assumptions about the ideal size of these regions for diagnosis.

They found that the algorithm made the most accurate classification of ��������𾱳����’s when the brain regions being compared were about 2250 to 3200 cubic millimetres. This was intriguing, says La Rocca, since this is similar to the size of the anatomical structures connected with the disease, such as the amygdala and hippocampus.

The team then tested the algorithm on a second set of scans from 148 subjects. Of these, 52 were healthy, 48 had ��������𾱳����’s disease and 48 had mild cognitive impairment (MCI) but were known to have developed ��������𾱳����’s disease 2.5 to nine years later.

The AI distinguished between a healthy brain and one with ��������𾱳����’s with an accuracy of 86 per cent. Crucially, it could also tell the difference between healthy brains and those with MCI with an accuracy of 84 per cent.

This shows that the algorithm could identify changes in the brain that lead to ��������𾱳����’s almost a decade before clinical symptoms appear. The researchers were limited by the scans available from the database, so they weren’t able to test whether the algorithm could predict the onset of disease even earlier.

Early diagnosis

��������𾱳����’s disease has been linked to the formation of sticky beta-amyloid plaques and neurofibrillary tau tangles in the brain. “Nowadays, cerebrospinal fluid analyses and brain imaging using radioactive tracers can tell us to what extent the brain is covered with plaques and tangles, and are able to predict relatively accurately who is at high risk of developing ��������𾱳����’s 10 years later,” says La Rocca. “However, these methods are very invasive, expensive and only available at highly specialised centres.”

In contrast, the new technique can distinguish with similar accuracy between brains that are normal and the brains of people with MCI who will go on to develop Alzheimer’s disease in about a decade – but using a simpler, cheaper and non-invasive technique. More work will be needed to distinguish between people with MCI whose brains go on to age normally, or who might develop other kinds of dementia.

Blood tests that look for biomarkers of ��������𾱳����’s could be even cheaper and simpler than the new technique, but none are on the market yet. “There are no blood tests for Alzheimer’s disease,” says at the University of Zagreb in Croatia. “There have been some attempts, but without much success yet.”

Next step

at the Icahn School of Medicine at Mount Sinai in New York is intrigued by the new test. He says that a method that might predict the disease a decade before it is fully expressed would be “incredibly valuable” should preventative therapeutics emerge.

La Rocca says her team now intends to extend the technique to help with the early diagnosis of other neurodegenerative conditions such as Parkinson’s disease. “It’s a method that is very versatile,” she says.

Reference: arXiv,