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Researchers to create AI models for early prediction of Alzheimer’s

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Researchers in the US have received a National Institutes of Health (NIH) grant to create predictive models for Alzheimer’s disease using brain MRIs, among other data sources.

The ultimate goal of the research is to enable earlier detection of Alzheimer’s—ideally two years or more before onset of symptoms—and identify patient populations at risk for developing the condition using MRI data, allowing researchers to test interventions and medications that interrupt the course of the disease.

To create these predictive models, the research team will use multimodal clinical data, including brain MRIs.

Madalina Fiterau is assistant professor in the Manning College of Information and Computer Sciences at UMass Amherst and principal investigator and project leader of the study

The researcher said: “This research brings us closer to putting people in clinical trials at a point where the brain biology is still intact and something can be done.

“Sixty per cent of a patient’s brain matter disappears by the time of diagnosis, and at that stage it’s irretrievable.

“What we would like to do is identify those changes early, at least two years before onset, and then, based on that, figure out which treatments work.”

The study’s other principal investigator, Joyita Dutta, associate professor of biomedical engineering, echoed this excitement of possible treatment on the horizon.

“We would not have been able to say this three years back, but now that many new drug candidates are emerging, we are at the point where forecasting techniques can actually be deployed to identify potential subjects for a disease-modifying therapy.”

Previous studies aimed to create deep-learning models for predicting degenerative disease using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

Unfortunately, this poses a challenge for making the findings generalisable as these ADNI-based models use engineered data.

For example, instead of incorporating actual images of the cerebral cortex, other studies use software to extract average cortical thickness.

Fiterau said: “ADNI contains a vast set of specialised features that are extracted from the brain images that require a lot of feature engineering and domain expertise.

“Data collected in the wild is not going to have the specialised features. You’re going to have the MRI scans, but no annotation.

“That’s the purpose of the grant: figuring out how to take a model that’s been trained on this specialized, carefully curated data set and see what its performance can be on real data collected in the wild.

The two-year grant is for $278,118 (£218,460).

The research is important because it enables predictive algorithms to use standard MRIs instead of requiring specialised data, explained Dutta.

She said: “I work extensively with PET scans for my research but not every clinic collects PET images of Alzheimer’s patients.

“At the same time, MRI tends to be the go-to imaging modality for individuals with neurological complaints.

“However, clinically available MRI scans are often conducted using protocols that are different from ADNI.

Predictive models, therefore, need to be generalisable to ‘data collected in the wild’ in order to be practically useful.”

The research team will use deep learning to extract features from standard brain MRIs that can stand in as proxies for the specialised features from the ADNI dataset.

There are some key regions that the research team know to be affected by Alzheimer’s—the hippocampus, cerebral cortex, and fluid-filled ventricle cavities—so the model will be trained to place a higher weight on these regions.

The research also aims to overcome model biases due to the demographic gaps in the ADNI data, namely the underrepresentation of minorities and the overrepresentation of highly educated individuals.

In this dataset, 93 per cent of the participants are white and 61 per cent have 16 years of education or more (the US average is 14 years).

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