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Researchers have developed an Artificial Intelligence (AI) -based computer algorithm that can accurately predict risk and diagnose Alzheimer’s disease using a combination of brain magnetic resonance imaging (MRI), tests to measure cognitive decline, 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 enhance the experience. Alzheimer’s disease is the leading cause of dementia worldwide. One in 10 people 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, then those technologies have great potential, especially in resource-limited settings. Not only can we accurately predict the risk of Alzheimer’s disease, but this algorithm can generate intuitive and interpretable visualizations of individual Alzheimer’s risk on the road to an accurate diagnosis. “
Vijaya B. Kolachalama, Ph.D., Corresponding Author, Assistant Professor of Medicine at Boston University School of Medicine (BUSM)
The researchers obtained access to raw MRI images of the brain, demographics, and clinical information from individuals with Alzheimer’s disease and those with normal cognition from four different national cohorts. Using data from one of these cohorts, they developed a new deep learning model to predict the risk of Alzheimer’s disease. They then showed that their model could accurately predict disease status in the other independent cohorts.
An international team of expert neurologists was then asked to perform the task of detecting Alzheimer’s disease in 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 the high-risk regions for disease identified by the model were highly aligned with autopsy reports from the brains of some deceased people.
According to the researchers, this study has broad implications for expanding the use of neuroimaging data such as magnetic resonance imaging to accurately detect the risk of Alzheimer’s disease at the point of care. “If we have accurate tools for predicting the risk of Alzheimer’s disease (like the one we developed) that are readily available and that can use routinely available data, such as an MRI of the brain, then they have the potential to help practice clinic, especially in memory clinics. “
The researcher believes that his methodology can be extended to other organs of the body and develop predictive models to diagnose other degenerative diseases.
These findings appear online in the journal. Brain.
Source:
Boston University School of Medicine
Journal reference:
Qiu, S, et al. (2020) Development and validation of an interpretable deep learning framework for the classification of Alzheimer’s disease. Brain. doi.org/10.1093/brain/awaa137.
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