Toronto: A team of scientists, including one of an Indian-origin, has successfully trained a new Artificial Intelligence (AI) algorithm that may soon help doctors to make accurate predictions regarding cognitive decline leading to Alzheimer's disease and provide intervention. The team, from the McGill University in Canada, designed an algorithm that learns signatures from magnetic resonance imaging (MRI), genetics, and clinical data. This specific algorithm can help predict whether an individual's cognitive faculties are likely to deteriorate towards Alzheimer's in the next five years. "At the moment, there are limited ways to treat Alzheimer's and the best evidence we have is for prevention. Our AI methodology could have significant implications as a'doctor's assistant' that would help stream people onto the right pathway for treatment," Mallar Chakravarty, assistant professor at the University's Department of Psychiatry.
Various researchers around the globe are developing ways to detect Alzheimer's as early as possible. After all, early detection gives people the power seek treatment that can slow down the condition's effects, as well as enough time to get their legal and financial affairs in order. Some decided to focus on blood and cerebrospinal fluid tests, while others are developing gadgets that can look for early signs. A team of researchers from the University of Bari in Italy, however, believe the answer lies in artificial intelligence. They trained their AI by feeding it 67 MRI scans -- 38 from Alzheimer's patients and 29 from healthy controls.
Pfizer and IBM researchers claim to have developed a machine learning technique that can predict Alzheimer's disease years before symptoms develop. By analyzing small samples of language data obtained from clinical verbal tests, the team says their approach achieved 71% accuracy when tested against a group of cognitively healthy people. Alzheimer's disease begins with vague, often misinterpreted signs of mild memory loss followed by a slow, progressively serious decline in cognitive ability and quality of life. According to the nonprofit Alzheimer's Association, more than 5 million Americans of all ages have Alzheimer's, and every state is expected to see at least a 14% rise in the prevalence of Alzheimer's between 2017 and 2025. Due to the nature of Alzheimer's disease and how it takes hold in the brain, it's likely that the best way to delay its onset is through early intervention.
Researchers from McGill University in Canada reveal how they used machine-learning techniques and beta-amyloid imaging to predict Alzheimer's development in patients with mild cognitive impairment (MCI) up to 2 years before symptoms arose. Co-lead study author Dr. Pedro Rosa-Neto, of the departments of Neurology & Neurosurgery and Psychiatry at McGill University, and colleagues recently reported their findings in the journal Neurobiology of Aging. MCI is a condition characterized by a decline in cognitive functions - such as memory and thinking skills - that is noticeable, but which does not impact a person's ability to carry out everyday tasks. According to the Alzheimer's Association, studies have suggested that around 15 to 20 percent of adults aged 65 and older are likely to have MCI, and these individuals are at greater risk of Alzheimer's than the general population. While the precise causes of MCI and Alzheimer's disease remain unclear, the accumulation of a protein called beta-amyloid is believed to play a major role.
A study published in the Journal of Medical Imaging unveils the use of machine learning to detect the early stages of Alzheimer's disease (AD) by functional magnetic resonance imaging. Alzheimer's disease is a neurodegenerative condition primarily occurring in late-adulthood and begins with symptoms of cognitive decline. Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the fMRI signals of healthy individuals, patients with mild cognitive impairment (MCI), and patients with Alzheimer's. "We present one such synergy of fMRI and deep learning, where we apply a simplified yet accurate method using a modified 3D convolutional neural networks to resting-state fMRI data for feature extraction and classification of Alzheimer's disease," the co-authors explained in their findings. "The convolutional neural network is designed in such a way that it uses the fMRI data with much less preprocessing, preserving both spatial and temporal information."