Cambridge scientists have developed an artificial intelligence tool capable of predicting in four out of five cases whether people with early signs of dementia will remain stable or develop Alzheimer’s disease.
The team says this new approach could reduce the need for invasive and expensive diagnostic tests while improving treatment outcomes at an early stage, when interventions such as lifestyle changes or new medications may have a chance to work best.
Dementia poses a major global health challenge, affecting more than 55 million people worldwide and costing an estimated $820 billion annually. The number of cases is projected to nearly triple over the next 50 years.
The leading cause of dementia is Alzheimer’s disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are most likely to be effective; however, early diagnosis and prognosis of dementia may not be accurate without the use of invasive or expensive tests, such as positron emission tomography (PET) or lumbar puncture, which are not available in all memory clinics. As a result, up to a third of patients may be misdiagnosed and others may be diagnosed too late for treatment to be effective.
A team led by scientists from the Department of Psychology at the University of Cambridge has developed a machine learning model capable of predicting whether and how quickly a person with mild memory and thinking problems will develop Alzheimer’s disease. In research published today in eClinical Medicinedemonstrate that it is more accurate than current clinical diagnostic tools.
To build their model, the researchers used routinely collected, noninvasive, and low-cost patient data (cognitive testing and structural MRI scans showing gray matter atrophy) from more than 400 individuals who were part of a research cohort in the U.S.
They then tested the model using real-life data from another 600 participants in the US cohort and, importantly, longitudinal data from 900 people from memory clinics in the UK and Singapore.
The algorithm was able to distinguish between people with stable mild cognitive impairment and those who progressed to Alzheimer’s disease over a three-year period. It was able to correctly identify people who developed Alzheimer’s in 82% of cases and correctly identify those who did not develop Alzheimer’s in 81% of cases from cognitive testing and an MRI alone.
The algorithm was approximately three times more accurate at predicting progression to Alzheimer’s than the current standard of care, i.e. standard clinical markers (such as grey matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce misdiagnoses.
The model also allowed the researchers to stratify people with Alzheimer’s disease using data from each person’s first visit to the memory clinic into three groups: those whose symptoms would remain stable (around 50% of participants), those who would progress slowly towards Alzheimer’s (around 35%), and those who would progress more rapidly (the remaining 15%). These predictions were validated by looking at follow-up data over 6 years. This is important as it could help identify those people at an early enough stage that they might benefit from new treatments, while also identifying people who need close monitoring as their condition is likely to deteriorate rapidly.
It is important to note that the 50% of people who present symptoms such as memory loss but remain stable would be better directed to a different clinical pathway, as their symptoms may be due to other causes besides dementia, such as anxiety or depression.
Lead author Professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge said: “We have created a tool that, 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 rapid or slow.
“This has the potential to significantly improve patient wellbeing by showing us which people need closer care, whilst removing anxiety for those patients who we predict will remain stable. At a time of intense pressure on healthcare resources, this will also help to remove the need for unnecessary invasive and costly diagnostic tests.”
Although the researchers tested the algorithm on data from a research cohort, they validated it using independent data that included nearly 900 people attending memory clinics in the UK and Singapore. In the UK, patients were recruited through the Quantitative MRI in NHS Memory Clinics (QMIN-MC) study, led by study co-author Dr Timothy Rittman of Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trusts (CPFT).
The researchers say this shows it should be applicable in a clinical setting with real-world patients.
Dr Ben Underwood, Honorary Consultant Psychiatrist at CPFT and Associate Professor in the Department of Psychiatry at the University of Cambridge, said: “Memory problems are common as we get older. In the 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 prefer to give definitive answers. The fact that we can reduce this uncertainty with the information we already have is exciting and is likely to become even more important as new treatments emerge.”
Professor Kourtzi said: “AI models are only as good as the data they are trained on. To ensure ours has the potential to be adopted in a healthcare setting, we trained and tested it on data routinely collected not only from research cohorts but also from patients in real-world memory clinics. This shows that it will be generalisable to a real-world setting.”
The team now hopes to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and use different types of data, such as blood test markers.
Professor Kourtzi added: “If we are to meet the growing healthcare challenge of dementia, we will need better tools to identify and intervene at the earliest possible stage. Our vision is to extend our AI tool to help clinicians match 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 the discovery of new drugs 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’s Cambridge Biomedical Research Centre.