How AI tools help predict Alzheimer’s disease early

A couple of recent studies in the US have shown that artificially intelligent (AI) tools can predict the progression of Alzheimer’s disease.

This new approach could reduce the need for invasive and costly diagnostic tests while improving treatment outcomes early when interventions such as lifestyle changes or new medicines may have a chance to work best.

Dementia poses a major global healthcare challenge, affecting more than 55 million globally at an estimated annual cost of $820 billion. The number of cases is expected to almost treble in the next 50 years.

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

Cambridge scientists last month developed an AI tool capable of predicting in four cases out of five whether people with early signs of dementia will remain stable or develop Alzheimer’s disease.

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 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 US.

They then tested the model using real-world patient data from 600 more 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.

“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,” senior author-professor Zoe Kourtzi from the Department of Psychology at the University of Cambridge, said.
“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.”

In another study in June, Boston University researchers said they built an AI tool that could predict with 80% accuracy if someone is at risk of developing Alzheimer’s disease based on their speech patterns.

The researchers used a natural language processing model to study if people with mild cognitive decline will develop Alzheimer’s in a six-year time frame. They observed a cohort of 166 people — 107 women and 59 men — between the ages of 63 and 97 who had some level of cognitive complaints.

Of these, 90 people developed progressive declines in their cognitive function, while 76 remained stable. The researchers discovered that by combining speech-recognition tools and machine learning, they could track links between speech patterns and cognitive decline, based on biomarkers associated with cognitive decline. The model they developed was able to predict significant cognitive decline with 78.5% accuracy, they said.

Though the sample used was small, indicating that such a tool is not meant to be leaned on as an exclusive method, one of its key strengths is its use of semantic features extracted from text data, which will allow the transfer of the AI tool to other languages, making it a valuable resource for global healthcare.

Additionally, the tool’s computer-aided decision-making capabilities can help mitigate interclinician variability in selecting participants for clinical trials and drug tests, ensuring more consistent and reliable patient selection processes, the researchers said.

This approach could lead to the development of a cost-effective and widely accessible remote screening tool, revolutionizing the early diagnosis and management of Alzheimer’s disease, the researchers added.