With an ageing global population, the prevalence of Alzheimer’s disease is rapidly increasing. It has created a heavy burden on public healthcare systems — especially in developing countries. It, therefore, becomes very critical to identify persons who are most likely to decline towards Alzheimer’s disease, in an effort to implement preventative treatments and interventions.
Now, researchers from the University of Toronto have designed an algorithm that learns signatures from magnetic resonance imaging (MRI), genetics, and clinical data. The research titled Modeling And Prediction Of Clinical Symptom Trajectories In Alzheimer’s Disease Using Longitudinal Data uses artificial intelligence algorithm which can accurately predict whether a person’s cognitive decline will lead to Alzheimer’s disease in the next five years or not.
The research by Indian-origin scientists Nikhil Bhagwat, M Mallar Chakravarty, and Joseph D Viviano and Aristotle N Voineskos, talks about their AI methodology could work symbiotically as a “doctor’s assistant”. Chakravarty, who is an assistant professor at the McGill University, Canada, told a newswire, “…This would help stream people onto the right pathway for treatment. For example, one could even initiate lifestyle changes that may delay the beginning stages of Alzheimer’s or even prevent it altogether.”
Reportedly, the researchers trained their algorithms using data from more than 800 people ranging from normal healthy seniors to those experiencing mild cognitive impairment, and Alzheimer’s disease patients.
Alzheimer’s is the most common form of dementia, a general term for memory loss and other cognitive abilities serious enough to interfere with daily life. Alzheimer’s disease accounts for 60 to 80 percent of dementia cases. The greatest known risk factor is increasing age, and the majority of people with Alzheimer’s are 65 and older. Unlike common perception, Alzheimer’s is not just a disease of old age. Over 200,000 Americans under the age of 65 have younger-onset of Alzheimer’s disease
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