Around the world, approximately 55 million people are dealing with dementia, as reported by the World Health Organization. The most common form of this challenging condition is Alzheimer's disease, which unfortunately is incurable and leads to a decline in brain function.
Alzheimer's doesn't just affect people physically; it brings about psychological, social, and economic challenges. It impacts not only those living with the disease but also their loved ones who care for them. Since the symptoms of Alzheimer's worsen over time, it becomes crucial for both patients and caregivers to prepare for increased support needs as the disease advances. Researchers at The University of Texas at Arlington recognized this necessity and have come up with a unique learning-based framework to assist Alzheimer's patients in identifying their position within the disease development spectrum accurately. This tool aids in predicting the timing of later stages, making it easier to plan for future care.
Dajiang Zhu, an associate professor in computer science and engineering at UTA and the lead author of a new peer-reviewed paper in Pharmacological Research, explained, "For decades, various predictive approaches have been proposed for Alzheimer’s disease and its precursor, mild cognitive impairment. Many of these earlier prediction tools overlooked the continuous nature of how Alzheimer’s disease develops and the transition stages of the disease."
With support from grants exceeding $2 million from the National Institutes of Health and the National Institute on Aging, Zhu's Medical Imaging and Neuroscientific Discovery research lab, in collaboration with Li Wang, UTA associate professor in mathematics, developed a novel learning-based embedding framework. This framework, termed a "disease-embedding tree" or DETree, efficiently and accurately codes the various stages of Alzheimer's disease development. Not only can DETree predict the five fine-grained clinical groups of Alzheimer's disease development, but it can also offer more detailed status information by projecting the patient's position within it as the disease progresses.
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