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Fresh from Genetic Engineering and Biotechnology News (GEN), here is a noteworthy Biotechnology article for you:
The early and accurate diagnosis of neurodegenerative diseases such as Alzheimer’s is critical to providing quality care to afflicted individuals. Yet, with one of the largest populations of individuals aging into the high-risk years for Alzheimer’s, improved diagnostic tools are imperative. In recent years, scientists and clinicians have focused their attention toward machine-learning artificial intelligence (AI) tools to help them diagnose Alzheimer’s risk and onset. Now, a team of investigators at Boston University School of Medicine (BUSM) may have developed just such an algorithm.
“Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages,” the authors of the newly published research wrote. “Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity.”
The BUSM researchers developed a computer algorithm based on AI that can accurately predict the risk for and diagnose Alzheimer’s disease using a combination of brain MRI, testing to measure cognitive impairment, along with data on age and gender. Findings from the new study were published recently in Brain through an article titled, “Development and validation of an interpretable deep learning framework for Alzheimer’s disease classification.”
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... Read the whole article at the Genetic Engineering and Biotechnology News (GEN) Blog.
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