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Fogged Memory

Fogged Memory

By Mark Watts

Mark Watts“Oh, yes this is so beautiful,” the 86-year-old mother of seven said as she gazed upon the Grand Canyon in Arizona.

I had just asked my mother if she was enjoying the view. This was a calm moment. Her eyes sparkled like they did after I had won a hurdle race or scored a winning touchdown.

Moments later, the fog of dementia rolled in and clouded our trip and she asked me, her son, “and who are you?”

This is the roller coaster of loving someone with Alzheimer’s disease.

When you have an intimate and personal concern about a familial disease, new research piques your interest.

Artificial intelligence in health care can be like a smartphone for telecommunications. A multi tooled platform with perfect memory, unlimited reach and tireless effort. The ability to review a brain scan at the pixel level and compare each pixel to the surrounding ones to recognize patterns unappreciated by humans.

Researchers are training an artificial intelligence (AI) system that can potentially diagnose dementia after a single brain scan.

The team, led by Professor Zoe Kourtzi, a Fellow at the Alan Turing Institute, and a professor of computational cognitive neuroscience at the University of Cambridge, has developed machine learning tools that can detect dementia in patients at a very early stage. Using brain scans from patients who went on to develop Alzheimer’s disease, their machine learning algorithm learned to spot structural changes in the brain. When combined with the results from standard memory tests, the algorithm was able to provide a prognostic score – that is, the likelihood of the individual having Alzheimer’s disease.

Currently, it can take several scans and tests to diagnose the disease. For those patients presenting with mild cognitive impairment – signs of memory loss or problems with language or visual/spatial perception – the algorithm was over 80% accurate in predicting those individuals who went on to develop Alzheimer’s disease. It was also able to predict how fast their cognition will decline over time.

Kourtzi said, “We have trained machine learning algorithms to spot early signs of dementia by looking for patterns of grey matter loss – essentially, wearing away – in the brain. When we combine this with standard memory tests, we can predict whether an individual will show slower or faster decline in their cognition.”

She goes on to say, “We’ve even been able to identify some patients who were not yet showing any symptoms but went on to develop Alzheimer’s. In time, we hope to be able to identify patients as early as five to 10 years before they show symptoms as part of a health check.”

Although the algorithm has been optimized to look for signs of Alzheimer’s disease, Kourtzi and colleagues are now training it to recognize different forms of dementia, each of which has its own characteristic pattern of volume loss.

Dr. Timothy Rittman from the department of clinical neurosciences and a consultant at Addenbrooke’s Hospital, part of Cambridge University Hospitals (CUH) NHS Foundation Trust, is leading a trial to look at whether this approach is useful in a clinical setting.

To date around 80 patients have taken part in the trial, which was run by CUH, Cambridgeshire and Peterborough NHS Foundation Trust and two NHS trusts in Brighton.

There are currently very few drugs available to help treat dementia. One of the reasons that clinical trials often fail is because it is thought that once a patient has developed symptoms, it may be too late to make a major difference. Having the ability to identify individuals at a very early stage could therefore help researchers develop new medicines.

If the trial is successful, the algorithm could be rolled out to thousands more patients across the country

If I was given a chance to know my mother’s slide into dementia was coming, I could have planned this trip years ago so that we could enjoy the view together.

Mark Watts is the enterprise imaging director at Fountain Hills Medical Center.

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