Artificial intelligence algorithm can accurately predict risk, diagnose AD
Artificial intelligence algorithm can accurately predict risk,
diagnose AD
by Boston University School of Medicine MAY 4,
2020
Researchers have developed a computer algorithm based on
Artificial Intelligence (AI) that can accurately predict the risk for and
diagnose Alzheimer's disease using a combination of brain magnetic resonance
imaging (MRI), testing to measure cognitive impairment, along with data on age
and gender.
The AI
strategy, based on a deep learning
algorithm, is a type of machine learning framework. Machine
learning is an AI application that enables a computer to learn from data and
improve from experience. Alzheimer's disease is the primary cause of dementia
worldwide. One in 10 people age 65 and older has Alzheimer's dementia. It is
the sixth-leading cause of death in the United States.
"If
computers can accurately detect debilitating conditions such as Alzheimer's
disease using readily available data such as a brain MRI scan, then such
technologies have a wide-reaching potential, especially in resource-limited
settings," explained corresponding author Vijaya B. Kolachalama, Ph.D.,
assistant professor of medicine at Boston University School of Medicine (BUSM).
"Not only can we accurately predict the risk of Alzheimer's disease but
this algorithm can generate interpretable and intuitive visualizations of
individual Alzheimer's disease risk en route to accurate diagnosis," said
Kolachalama.
The
researchers obtained access to raw MRI scans of the brain, demographics and
clinical information of individuals with Alzheimer's disease and the ones with
normal cognition from four different national cohorts. Using data from one of
these cohorts, they developed a novel deep learning model to predict
Alzheimer's disease risk. They then showed that their model could accurately
predict the disease status on the other independent cohorts.
An
international team of expert neurologists were then asked to perform the task
of detecting Alzheimer's disease on the same set of cases. In this head-to-head comparison, the algorithm model
performed slightly better than the average neurologist. They also showed that
model-identified regions of high disease risk were highly aligned with autopsy
reports of the brains on a few individuals who were deceased.
According
to the researchers, this study has broad implications for expanding the use of
neuroimaging data such as MRI scans to accurately detect the risk of
Alzheimer's disease at the point of care. "If we have accurate tools to
predict the risk of Alzheimer's disease (such as the one we developed), that
are readily available and which can use routinely available data such as a
brain MRI scan, then they have the potential to assist clinical practice,
especially in memory clinics."
The
researchers believe their methodology can be extended to other organs in the
body and develop predictive models to diagnose other degenerative diseases.
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