IBM has partnered with pharmaceutical giant Pfizer to design an artificial intelligence (AI) model to predict the eventual onset of the neurological disease seven years before symptoms appear. Alzheimer's is currently incurable and is often diagnosed too late to prevent it from accelerating. Symptoms for the disease include the gradual degradation of memory, confusion, and difficulty in completing once-familiar daily tasks. Published in The Lancet eClinical Medicine, the researchers used small samples of language data from clinical verbal tests provided by the Framingham Heart Study, a long-term study that has been tracking the health of more than 5,000 people and their families since 1948, to train the AI models. The AI model's ability was then verified against data samples from a group of healthy individuals who eventually did and did not develop the disease later in life.
PHILADELPHIA – As the search for successful Alzheimer's disease drugs remains elusive, experts believe that identifying biomarkers -- early biological signs of the disease -- could be key to solving the treatment conundrum. However, the rapid collection of data from tens of thousands of Alzheimer's patients far exceeds the scientific community's ability to make sense of it. Now, with a $17.8 million grant from the National Institute on Aging at the National Institutes of Health, researchers in the Perelman School of Medicine at the University of Pennsylvania will collaborate with 11 research centers to determine more precise diagnostic biomarkers and drug targets for the disease, which affects nearly 50 million people worldwide. For the project, the teams will apply advanced artificial intelligence (AI) methods to integrate and find patterns in genetic, imaging, and clinical data from over 60,000 Alzheimer's patients -- representing one of the largest and most ambitious research undertakings of its kind. Penn Medicine's Christos Davatzikos, PhD, a professor of Radiology and director of the Center for Biomedical Image Computing and Analytics, and Li Shen, PhD, a professor of Informatics, will serve as two of five co-principal investigators on the five-year project.
Are GANs the next step in Deep Learning? Well, the subset of Machine Learning was once described by Yoshua Bengio as the most interesting idea in the last 10 years of ML, with the technique of using two neural networks against each other to generate new, synthetic instances of data that can pass for real data, opening many doors in the world of AI. That said, we wanted to explore some of the applications of GANs currently being used through the below 5 must-watch presentations from DeepMind, NASA, MIT, Insitro and Université de Montréal. In this presentation, Francesco introduces a new deep generative model for the genetic analysis of medical imaging, combining both convolutional neural networks and structured linear mixed models to extract latent imaging features in the context of genetic association studies. The linked presentation includes an application of the method to brain MRI images from the Alzheimer's Disease Neuroimaging Initiative dataset, where we reveal novel and known risk genes for neurological and psychiatric disorders. Genetic association studies and the process of evaluation during study is covered before looking at both the phenotypes and genetic variants of participants.
This ability of AI to spot patterns using data has led to a new form of data for research called digital biomarkers, which will accelerate our understanding of Alzheimer's disease. A digital biomarker is a quantifiable measure of a person's physiological and/or behavioural state captured through connected devices, including wearables (such as blood pressure monitors or sensors), implantable devices (such as pacemakers or continuous glucose monitors) or smart devices in the home (such as voice assistants or gait sensors). They're also sometimes referred to as digital signatures or fingerprints because their exact values tend to be unique to us. Identifying those digital biomarkers or combinations of biomarkers that indicate early risk of Alzheimer's, such as certain sleep patterns, nocturnal blood pressure dipping, or changes in voice analysis, will help design possible interventions – whether lifestyle or drug – to delay, or one day even cure the disease.
Tiny robots that can transport individual neurons and connect them to form active neural circuits could help us study brain disorders such as Alzheimer's disease. The robots, which were developed by Hongsoo Choi at the Daegu Gyeongbuk Institute of Science and Technology in South Korea and his colleagues, are 300 micrometres long and 95 micrometre wide. They are made from a polymer coated with nickel and titanium and their movement can be controlled with external magnetic fields.
Frances E. Allen, an American computer scientist, ACM Fellow, and the first female recipient of the ACM A.M. Turing Award (2006), passed away on Aug. 4, 2020--her 88th birthday--from complications of Alzheimer's disease. Allen was raised on a dairy farm in Peru, NY, without running water or electricity. She received a BS degree in mathematics from the New York State College for Teachers (now the State University of New York at Albany). Inspired by a beloved math teacher, and by the example of her mother, who had also been a grade-school teacher, Allen started teaching high school math. She needed a master's degree to be certified, so she enrolled in a mathematics master's program at the University of Michigan.
An artificial intelligence (AI) algorithm has produced another significant breakthrough using attention mechanisms and a convolutional neural network to accurately identify tell-tale signs of Alzheimer's. The AI tool developed by the Stevens Institute of Technology is said to be able to explain its conclusions, thus enabling human experts to check the accuracy of its diagnosis by up to 95%. AI has made huge strides in the medical sector and this latest news is further evidence that the speed at which the technology is moving shows no signs of ceasing any time soon. The algorithm is trained to identify subtle linguistic patterns previously overlooked by using texts composed by both healthy subjects and known Alzheimer's sufferers. The team of researchers then converted each sentence into a unique numerical sequence, or vector, representing a specific point in a 512-dimensional space.
Neurological conditions such as Parkinson's and Alzheimer's could be diagnosed from simple eye scans performed by high street opticians thanks to a new NHS artificial intelligence (AI) project. Newcastle University is working on the project with medics at North East hospitals as part of a national £50 million boost to use AI in a range of health schemes. Early diagnosis in progressive neurological diseases such as Parkinson's and Alzheimer's, which affect more than one million people in the UK, is important, so speeding up the process could be crucial. Anya Hurlbert, professor of visual neuroscience at Newcastle University, is leading the Octahedron project. She said: "The retina at the back of the eye is basically an outpost of the brain and the only part of the central nervous system we can see directly from the outside. "We know that in Alzheimer's disease and Parkinson's disease the retina is affected." Very detailed images of the retina can be captured by optical coherence tomography, or OCT scanning, which is quick and cheap and increasingly available at high street opticians. Further analysis of these scans will now be developed with the use of AI, to recognise signs of neurological disease. Prof Hurlbert said: "The aim of the project is to use NHS data to teach computers how to detect early signs of neurological disease via retinal imaging.