If you’re worried that the computers are getting smarter, then you might just be right as a machine has successfully learned how to diagnose Alzheimer’s and is now more accurate than any other method.

The machine learning program, which gets more intelligent over time, is able to make a diagnosis before the commonly known symptoms (such as forgetting where you put your keys) start to become noticeable in everyday life.

And as Alzheimer’s has no cure, early diagnosis is key to preserving patient’s independence.

Developed by researchers at Case Western Reserve University, the smart algorithm (known as Cascaded Multi-view Canonical Correlation or CaMCCo) takes measurements from MRI scans.

And then in a two-stage review it examines the the hippocampus, glucose metabolism rates in the brain, proteomics, genomics, mild cognitive impairment and other parameters in order to predict whether the brain is healthy or has early-onset dementia.

Professor Anant Madabhush said: “We deliberately included mild cognitive impairment, which can be a precursor to Alzheimer’s, but not always.”

First, it selects the parameters that best distinguish between someone who’s healthy and someone who’s not. And second, it selects from the unhealthy variables those that best distinguish who has mild cognitive impairment and who has Alzheimer’s disease.

“The algorithm assumes each parameter provides a different view of the disease, as if each were a different set of colored spectacles,” said Madabhush. 

When it was tested on 149 patients CaMCCo outperformed individual indicators as well as methods that combine them all without selective assessment.

It also was better at predicting who had mild cognitive impairment than other methods that combine multiple indicators. 

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Alzheimer’s disease is the most common type of dementia, a progressive and irreversible neurological disease which affects multiple brain functions and affects an estimated 850,000 people in the UK. 

The exact cause is unknown although a number of things are thought to increase likelihood including old age, a family history of the condition, previous severe head injuries and lifestyle factors associated with cardiovascular disease.

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