The researchers applied machine learning methods to special type of MRI called arterial spin labelling imaging. (Representational image)
Combining machine learning method -- a type of artificial intelligence -- with a special MRI technique may help physicians predict who is more likely to develop Alzheimer's disease, a study says.
Machine learning is a type of artificial intelligence that allows computer programmes to learn when exposed to new data without being programmed.
"With standard diagnostic MRI, we can see advanced Alzheimer's disease, such as atrophy of the hippocampus," said principal investigator Alle Meije Wink from VU University Medical Centre in Amsterdam.
"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," Meije Wink explained.
For the new study, published online in the journal Radiology, 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 (unseen) cases.
The study included 260 of 311 participants from the Alzheimer Center of the VU University Medical Center dementia cohort 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.
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, the researchers were then 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.
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