AI Beats Clinical Tests in Forecasting Alzheimer's Progression

Published Date: 13/07/2024

As AI becomes increasingly pervasive in our lives, it's essential to consider the potential pitfalls and ensure its ethical development and deployment.

A new machine learning model can predict whether and how fast an individual with mild memory and thinking problems will progress to developing Alzheimer's disease, outperforming current clinical diagnostic tools. "Dementia poses a significant global healthcare challenge, affecting over 55 million people worldwide at an estimated annual cost of $820 billion. The number of cases is expected to almost treble over the next 50 years. The main cause of dementia is Alzheimer's disease, which accounts for 60-80% of cases. Early detection is crucial as this is when treatments are likely to be most effective, yet early dementia diagnosis and prognosis may not be accurate without the use of invasive or expensive tests such as positron emission tomography (PET) scans or lumbar puncture, which are not available in all memory clinics.

A team led by scientists from the Department of Psychology at the University of Cambridge has developed a machine learning model able to predict whether and how fast an individual with mild memory and thinking problems will progress to developing Alzheimer's disease. In research published today in eClinical Medicine, they show that it is more accurate than current clinical diagnostic tools.

To build their model, the researchers used routinely-collected, non-invasive, and low-cost patient data – cognitive tests and structural MRI scans showing grey matter atrophy – from over 400 individuals who were part of a research cohort in the USA.

They then tested the model using real-world patient data from a further 600 participants from 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 within a three-year period. It was able to correctly identify individuals who went on to develop Alzheimer's in 82% of cases and correctly identify those who didn’t in 81% of cases from cognitive tests and an MRI scan alone.

The algorithm was around three times more accurate at predicting the progression to Alzheimer's than the current standard of care; that is, standard clinical markers (such as grey matter atrophy or cognitive scores) or clinical diagnosis. This shows that the model could significantly reduce misdiagnosis.

The model also allowed the researchers to stratify people with Alzheimer's disease using data from each person's first visit at the memory clinic into three groups  those whose symptoms would remain stable (around 50% of participants), those who would progress to Alzheimer's slowly (around 35%) and those who would progress more rapidly (the remaining 15%). These predictions were validated when looking at follow-up data over 6 years.

Importantly, those 50% of people who have 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 rather than dementia, such as anxiety or depression.

The researchers say this shows it should be applicable in a real-world patient, clinical setting.

The team now hope to extend their model to other forms of dementia, such as vascular dementia and frontotemporal dementia, and using different types of data, such as markers from blood tests.

The University of Cambridge is a world-leading research institution with a strong track record of innovation and discovery. The Department of Psychology is a leading centre for research and teaching in psychology, with a strong focus on understanding the human brain and behaviour. The University of Cambridge is a world-leading research institution with a strong track record of innovation and discovery.

FAQS:

Q: What is the main cause of dementia?

A: The main cause of dementia is Alzheimer's disease, which accounts for 60-80% of cases.

Q: Why is early detection of Alzheimer's disease crucial?

A: Early detection is crucial as this is when treatments are likely to be most effective.

Q: What type of data was used to build the machine learning model?

A: The researchers used routinely-collected, non-invasive, and low-cost patient data – cognitive tests and structural MRI scans showing grey matter atrophy – from over 400 individuals.

Q: How accurate was the algorithm at predicting the progression to Alzheimer's?

A: The algorithm was able to correctly identify individuals who went on to develop Alzheimer's in 82% of cases and correctly identify those who didn’t in 81% of cases.

Q: What is the potential impact of this research on patient wellbeing?

A: 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.

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