Published in eClinical Medicine, the study describes how the team built an algorithm using cognitive tests and MRI scans showing grey matter atrophy in over 400 individuals from a research cohort in the US. The AI model was then tested using real-world patient data from a further 600 participants from the same US cohort, as well as longitudinal data from 900 people from memory clinics in the UK and Singapore.
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The algorithm could distinguish between stable mild cognitive impairment and those who progressed to Alzheimer’s disease within a three-year period. It was able to correctly predict the development of Alzheimer’s in 82 per cent of cases and correctly identify those who did not develop the disease in 81 per cent of cases – figures roughly three times more accurate than current clinical diagnostics.
“We’ve created a tool which, despite using only data from cognitive tests and MRI scans, is much more sensitive than current approaches at predicting whether someone will progress from mild symptoms to Alzheimer’s – and if so, whether this progress will be fast or slow,” said senior author Professor Zoe Kourtzi, from Cambridge’s Department of Psychology.
“This has the potential to significantly improve patient wellbeing, showing us which people need closest care, while removing the anxiety for those patients we predict will remain stable. At a time of intense pressure on healthcare resources, this will also help remove the need for unnecessary invasive and costly diagnostic tests.”
As well as delivering major leap forward in accuracy, the model could reduce the need for invasive testing like positron emission tomography (PET) scans or lumbar puncture, helping to improve outcomes due to early medical and lifestyle interventions. The team now hopes to apply the technology to other forms of dementia - such as vascular dementia and frontotemporal dementia - as well as introduce different data types into the model, such as markers from blood tests.
“If we’re going to tackle the growing health challenge presented by dementia, we will need better tools for identifying and intervening at the earliest possible stage,” said Professor Kourtzi.
“Our vision is to scale up our AI tool to help clinicians assign the right person at the right time to the right diagnostic and treatment pathway. Our tool can help match the right patients to clinical trials, accelerating new drug discovery for disease modifying treatments.”
The study was funded by Wellcome, the Royal Society, Alzheimer’s Research UK, the Alzheimer’s Drug Discovery Foundation Diagnostics Accelerator, the Alan Turing Institute, and the National Institute for Health Research Cambridge Biomedical Research Centre.
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