Cambridge research finds AI can improve diagnosis of Alzheimer’s disease

Researchers at CPFT – Cambridgeshire and Peterborough NHS Foundation Trust – and the University of Cambridge have revealed the breakthrough.

Dementia is a significant global healthcare challenge, affecting over 55 million people worldwide with the number of cases expected to almost treble over the next 50 years, so early detection is crucial. The main cause of dementia is Alzheimer’s disease, which accounts for 60-80 per cent of cases.

The new tool is able to identify whether people with early signs of dementia will remain stable or develop this condition in four out of five cases, using routinely collected data.

This novel approach can reduce the need for invasive and costly diagnostic tests like lumbar punctures while improving treatment outcomes by indicating when early interventions like lifestyle changes or new medicines will work best, the researchers say.

CPFT Research and Development Director Dr Ben Underwood worked with the Trust’s memory clinics staff and patients on this study, supported by the Windsor Research Unit.

Dr Underwood said: “Memory problems are common as we get older. In clinic I see how uncertainty about whether these might be the first signs of dementia can cause a lot of worry for people and their families, as well as being frustrating for doctors who would much prefer to give definitive answers.

“The fact that we might be able to reduce this uncertainty with information we already have is exciting and is likely to become even more important as new treatments emerge.”

The research team developed a machine learning model able to predict if an individual with mild memory and thinking problems will develop Alzheimer’s disease – and how quickly. Their findings, published in eClinical Medicine, show that this method is more accurate than current clinical diagnostic tools.

Senior author Professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge said: “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.

"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.”

The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer’s disease within a three-year period.

It was able to correctly identify individuals who went on to develop Alzheimer’s in 82 per cent of cases and those who didn’t in 81 per cent of cases and was around three times more accurate than standard clinical markers, the researchers say.

The model can identify who would benefit from new dementia treatments as soon as possible and who needs close monitoring as their condition is likely to deteriorate rapidly, as well as who may required a different clinical care pathway for their symptoms.

The algorithm was tested on data from a research cohort but was also validated using independent data that included almost 900 individuals who attended memory clinics in the UK and Singapore.

In the UK, patients were recruited through the Quantitative MRI in NHS Memory Clinics Study (QMIN-MC) led by study co-author Dr Timothy Rittman at CPFT and Cambridge University Hospitals NHS Foundation Trust.

The research team say this data shows it should be applicable in a real-world patient, clinical setting. They now hope to extend their model to other forms of dementia (vascular and frontotemporal) using different types of data, such as markers from blood tests.

They collaborated with a cross-disciplinary team including Professor Peter Tino at the University of Birmingham and Professor Christopher Chen at the National University of Singapore.

Their research was supported by CPFT’s memory clinics, 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 and Care Research Cambridge Biomedical Research Centre.

Cambridgeshire and Peterborough NHS Foundation Trust (CPFT) is a health and social care organisation, providing integrated community, mental health and learning disability services, across Cambridgeshire and Peterborough, and children’s community services in Peterborough.

It supports a population of just under a million people and employs nearly 4,500 staff. Its largest bases are at the Cavell Centre in Peterborough, and Fulbourn Hospital, Cambridge, but staff are based across more than 50 locations.

The organisation is a University of Cambridge Teaching Trust and member of Cambridge University Health Partners, working together with the University of Cambridge Clinical School and Anglia Ruskin University.

Together with global, national and local partners CPFT conducts high-quality and ground-breaking research into mental and physical health and support innovation to improve patient care.