Scientists have developed a new artificial intelligence system that can detect early forms of Alzheimer's disease by combining machine learning methods with a special MRI technique.
Machine learning is a type of artificial intelligence that allows computer programmes to learn when exposed to new data without being programmed.
Researchers coupled machine learning methods with a special magnetic resonance imaging (MRI) technique that measures the perfusion, or tissue absorption rate, of blood throughout the brain to detect early forms of dementia, such as mild cognitive impairment (MCI).
"MRI can help with the diagnosis of Alzheimer's disease. However, the early diagnosis of Alzheimer's disease is problematic," said Alle Meije Wink, from the VU University Medical Centre in Amsterdam.
Scientists have long known that Alzheimer's disease is a gradual process and that the brain undergoes functional changes before the structural changes associated with the disease show up on imaging results.
Physicians have no definitive way of identifying who has early dementia or which cases of mild cognitive impairment will progress to Alzheimer's disease.
"With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," Meije Wink said.
"But at that point, the brain tissue is gone and there's no way to restore it. It would be helpful to detect and diagnose the disease before it's too late," he said.
The researchers applied machine learning methods to special type of MRI called arterial spin labelling (ASL) imaging.
ASL MRI is used to create images called perfusion maps, which show how much blood is delivered to various regions of the brain.
The automated machine learning programme is taught to recognise patterns in these maps to distinguish among patients with varying levels of cognitive impairment and predict the stage of Alzheimer's disease in new cases.
The study included 260 of 311 participants who underwent ASL MRI between October 2010 and November 2012.
The study group included 100 patients diagnosed with probable Alzheimer's disease, 60 patients with mild cognitive impairment (MCI) and 100 patients with subjective cognitive decline (SCD) and 26 healthy controls.
SCD and MCI are considered to be early stages of the dementia process and are diagnosed based on the severity of cognitive symptoms, including memory loss and decision-making problems.
The automated system was able to distinguish effectively among participants with Alzheimer's disease, MCI and SCD.
Using classifiers based on the automated machine learning training, researchers were able to predict the Alzheimer's diagnosis or progression of single patients with a high degree of accuracy, ranging from 82 per cent to 90 per cent.
The study was published in the journal Radiology.
