Artificial intelligence outperforms clinical trials in predicting Alzheimer's disease progression

Scientists at the University of Cambridge have developed an artificial intelligence tool that can predict whether people with early signs of dementia will remain stable or develop Alzheimer's in four out of five cases.

The researchers say this new approach could reduce the need for invasive and costly diagnostic tests and improve outcomes at an earlier stage, when interventions such as lifestyle changes and new drugs may be most effective.

Dementia is a major global healthcare challenge, affecting more than 55 million people worldwide and costing an estimated $820 billion annually, with the number of patients expected to nearly triple over the next 50 years.

The leading cause of dementia is Alzheimer's disease, accounting for 60-80% of cases. Early detection is important because treatment is likely to be most effective, but early diagnosis and prognosis of dementia may not be accurate without invasive or expensive tests such as a positron emission tomography (PET) scan or lumbar puncture, which are not available at all memory clinics.

As a result, up to a third of patients may be misdiagnosed, and others may receive a diagnosis too late to benefit from treatment.

A team led by scientists from the University of Cambridge's School of Psychology has developed a machine learning model that can predict whether people with mild memory or thought disorders will develop Alzheimer's disease, and how quickly they will develop it. Clinical Medicineand has been shown to be more accurate than current clinical diagnostic tools.

To build the model, the researchers used routinely collected, non-invasive, low-cost patient data.Cognitive testing Structural MRI scans showing grey matter atrophy from over 400 subjects in a US study cohort.

Then they Patient Data Additionally, data were obtained from 600 participants from a 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 within three years. Using only cognitive tests and MRI scans, it correctly identified those who went on to develop Alzheimer's disease in 82% of cases and those who did not in 81% of cases.

The algorithm was approximately three times more accurate at predicting progression to Alzheimer's disease than the current standard of care, i.e. standard clinical markers (such as gray matter atrophy and cognitive scores) and clinical diagnosis, indicating that the model can significantly reduce misdiagnosis.

This model allowed the researchers to classify Alzheimer's patients into three groups, using data from each person's first visit to the memory clinic: those whose symptoms remained stable (about 50% of participants), those whose Alzheimer's disease progressed more slowly (about 35%), and those whose Alzheimer's disease progressed more rapidly (the remaining 15%).

These predictions were validated by looking at six years of follow-up data, which is important because it helps identify people early on who may benefit from new treatments, as well as those who need closer monitoring because their condition may worsen quickly.

Importantly, the 50% of people who have symptoms such as memory loss but remain stable are better off being directed down a different clinical pathway as their symptoms may be caused by something other than dementia, such as anxiety or depression.

Lead author Professor Zoe Curzi, from the University of Cambridge's School of Psychology, said: “Despite using only data from cognitive tests and MRI scans, we have developed a tool that is far more sensitive than current approaches in predicting whether mild symptoms will progress to Alzheimer's disease, and if so, whether that progression will be fast or slow.”

“This has the potential to significantly improve patient health outcomes by showing us which patients need the most attention and removing anxiety for those who are predicted to remain in a stable condition. At a time of increasing pressure on healthcare resources, this will also help to eliminate the need for unnecessary invasive and costly diagnostic tests.”

The researchers tested the algorithm on data from the study cohort, but the algorithm was then validated using independent data including about 900 people who attended memory clinics in the UK and Singapore.

In the UK, patients were recruited through the NHS Memory Clinic Quantitative MRI Study (QMIN-MC), led by study co-author Dr Timothy Littman from Cambridge University Hospitals NHS Trust and Cambridgeshire and Peterborough NHS Foundation Trust (CPFT).

The researchers say this shows potential application to real patients and clinical settings.

Dr Ben Underwood, CPFT, Honorary Consultant Psychiatrist and Associate Professor of Psychiatry at the University of Cambridge, said: “Memory loss is a common symptom as people get older. In clinical practice we see that the uncertainty of whether this is the first sign of dementia can cause great anxiety for patients and their families, as well as frustrating situations for doctors who want to give definitive answers.”

“The fact that information we already have can potentially reduce this uncertainty is encouraging and is likely to become even more important as new treatments emerge.”

“An AI model is only as good as the data it is trained on,” said Professor Kurzi. “To validate our model's potential for clinical adoption, we trained and tested it not only on a research cohort, but also on routinely collected data from real memory clinic patients. This shows that our AI model can be generalized to the real world.”

The team now hopes to extend the model to other types of dementia, such as vascular and frontotemporal dementia, using different types of data, such as blood test markers.

“If we are to address the health challenges we face, dementiaWe need better tools to identify and intervene at the earliest possible stage.

“Our vision is to extend AI tools to help clinicians put the right person on the right diagnosis and treatment pathway at the right time. Our tools will help match the right patients to clinical trials and accelerate the discovery of new medicines for disease-modifying treatments.”