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AI detects protein signatures for Alzeihmer's disease in the blood - Advanced Science News

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Nanoparticles could make a reliable blood test for Alzheimer's disease a reality; image credit: National Cancer Institute, Daniel Sone Using nanoparticles with different surface properties, researchers are able to detect subtle changes in the composition of proteins in the plasma years before the presentation of clinical symptoms of Alzheimer's disease, which include memory loss, confusion, and cognitive difficulties. Owing to the unique properties of nanoparticles, different proteins in biological fluids selectively stick onto their surface forming a protein corona, which was found to change during disease. Researchers from the United States and Italy identify these subtle changes in plasma protein patterns to distinguish plasma samples from healthy individuals and those diagnosed with Alzheimer's disease. "Protein corona composition is both influenced by specific health conditions as well as the chemical and physical properties of the nanoparticles themselves," says Dr. Claudia Corbo of the University of Milano-Bicocca and lead author of the study published in Advanced Healthcare Materials. "Binding of proteins to the surface of particles is very precise and dependent on the chemistry and shape of the particles and the chemistry and structure of the proteins," says senior author Professor Omid Farokhzad of Brigham and Women's Hospital and Harvard Medical School.


Irish researcher develops AI to help prevent sight loss

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The ability to apply artificial intelligence (AI) to ophthalmology is gathering pace, a consequence of remarkable collaboration between eye specialists and technologists whose forte is the ability to process vast amounts of data quickly. Irish ophthalmologist Dr Pearse Keane – based in Moorfields Hospital, London – has been the chief catalyst in developing AI software to detect 50 sight-threatening eye diseases. It operates by interpreting optical coherence tomography (OCT) scans of the back of the eye, which soon will be routine when going for an eye check. Automation in analysing scans for diseases such as wet age-related macular degeneration (AMD), the main cause of blindness in Europe, and diabetic retinopathy, is about to revolutionise patient outcomes with faster results affording earlier diagnosis and prompt treatment, and ultimately preventing avoidable sight loss. Since that initial breakthrough, the Keane team has developed an alert system for a third of people with AMD who later get it in their good eye and, potentially, an early-warning system for onset of neurodegenerative diseases, notably Alzheimer's.


How a machine learning algorithm could identify the early stages of Alzheimer's in patients - Mental Daily

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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."


Artificial intelligence-based algorithm for the early diagnosis of Alzheimer's

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Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders. It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons.


How a machine learning algorithm could identify the early stages of Alzheimer's in patients

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Researchers from Texas Tech University developed a deep-learning algorithm called a convolutional neural network able to distinguish between the …


Artificial intelligence-based algorithm for the early diagnosis of Alzheimer's

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Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders. It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons.


Artificial intelligence-based algorithm for the early diagnosis of Alzheimer's

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IMAGE: Network activation map from the output of second temporal convolution layer mapped onto MNI brain atlas. Alzheimer's disease (AD) is a neurodegenerative disorder that affects a significant proportion of the older population worldwide. It causes irreparable damage to the brain and severely impairs the quality of life in patients. Unfortunately, AD cannot be cured, but early detection can allow medication to manage symptoms and slow the progression of the disease. Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders.


Detecting Alzheimer's Earlier with the Help of Machine-Learning Algorithm

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Functional magnetic resonance imaging (fMRI) is a noninvasive diagnostic technique for brain disorders, such as Alzheimer's disease (AD). It measures minute changes in blood oxygen levels within the brain over time, giving insight into the local activity of neurons; however, fMRI has not been widely used in clinical diagnosis. Their limited use is due to the fact fMRI data are highly susceptible to noise, and the fMRI data structure is very complicated compared to a traditional x-ray or MRI scan. Scientists from Texas Tech University now report they developed a type of deep-learning algorithm known as a convolutional neural network (CNN) that can differentiate among the fMRI signals of healthy people, people with mild cognitive impairment, and people with AD. Their findings, "Spatiotemporal feature extraction and classification of Alzheimer's disease using deep learning 3D-CNN for fMRI data," is published in the Journal of Medical Imaging and led by Harshit Parmar, doctoral student at Texas Tech University.


Clinical trials platform based on machine learning launched by – IAM Network

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A new platform aimed at streamlining clinical trials in order to lower costs – and increase the efficiency and success rate of a drug or the development of a medical device – has been unveiled by Ben-Gurion University of the Negev (BGU).The yet-to-be-named programme reportedly "leverages machine learning to optimise a clinical trial's chances of success" through the analysis of factors including patient population recruitment and dropout rate, and the identification of monitored markers. In turn, the platform provides pre-trial recommendations, in-trial interim analysis, and post-trial insights for "next trial preparation, as well as potential salvage options in case of failure," the Israeli public research university said in a statement. HIMSS20 Digital Learn on-demand, earn credit, find products and solutions. Panacea, a new company founded by BGN Technologies – the technology transfer company of the university – is licensing the technology for development and commercialisation. It is said to have been already used in the clinical studies of various neurodegenerative disorders, including amyotrophic lateral sclerosis (ALS), Parkinson's disease, and Alzheimer's disease.


USC leads massive new artificial intelligence study of Alzheimer's

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A massive problem like Alzheimer's disease (AD) — which affects nearly 50 million people worldwide — requires bold solutions. New funding expected to total $17.8 million, awarded to the Keck School of Medicine of USC's Mark and Mary Stevens Neuroimaging and Informatics Institute (INI) and its collaborators, is one key piece of that puzzle. The five-year National Institutes of Health (NIH)-funded effort, "Ultrascale Machine Learning to Empower Discovery in Alzheimer's Disease Biobanks," known as AI4AD, will develop state-of-the-art artificial intelligence (AI) methods and apply them to giant databases of genetic, imaging and cognitive data collected from AD patients. Forty co-investigators at 11 research centers will team up to leverage AI and machine learning to bolster precision diagnostics, prognosis and the development of new treatments for AD. "Our team of experts in computer science, genetics, neuroscience and imaging sciences will create algorithms that analyze data at a previously impossible scale," said Paul Thompson, PhD, associate director of the INI and project leader for the new grant. "Collectively, this will enable the discovery of new features in the genome that influence the biological processes involved in Alzheimer's disease." Predicting a diagnosis The project's first objective is to identify genetic and biological markers that predict an AD diagnosis — and to distinguish between several subtypes of the disease. To accomplish this, the research team will apply sophisticated AI and machine learning methods to a variety of data types, including tens of thousands of brain images and whole genome sequences. The investigators then will relate these findings to the clinical progression of AD, including in patients who have not yet developed dementia symptoms. The researchers will train AI methods on large databases of brain scans to identify patterns that can help detect the disease as it emerges in individual patients. "As we get older, each of us has a unique mix of brain changes that occur for decades before we develop any signs of Alzheimer's disease — changes in our blood vessels, the buildup of abnormal protein deposits and brain cell loss," said Thompson, who also directs INI's Imaging Genetics Center. "Our new AI methods will help us determine what changes are happening in each patient, as well as drivers of these processes in their DNA, that we can target with new drugs." The team is even creating a dedicated "Drug Repurposing Core" to identify ways to repurpose existing drugs to target newly identified segments of the genome, molecules or neurobiological processes involved in the disease. "We predict that combining AI with whole genome data and advanced brain scans will outperform methods used today to predict Alzheimer's disease progression," Thompson said. Advancing AI The AI4AD effort is part of the "Cognitive Systems Analysis of Alzheimer's Disease Genetic and Phenotypic Data" and "Harmonization of Alzheimer's Disease and Related Dementias (AD/ADRD) Genetic, Epidemiologic, and Clinical Data to Enhance Therapeutic Target Discovery" initiatives from the NIH's National Institute on Aging. These initiatives aim to create and develop advanced AI methods and apply them to extensive and harmonized rich genomic, imaging and cognitive data. Collectively, the goals of AI4AD leverage the promise of machine learning to contribute to precision diagnostics, prognostication, and targeted and novel treatments. Thompson and his USC team will collaborate with four co-principal investigators at the University of Pennsylvania, the University of Pittsburgh and the Indiana University School of Medicine. The researchers will also host regular training events at major AD neuroimaging and genetics conferences to help disseminate newly developed AI tools to investigators across the field. Research reported in this publication will be supported by the National Institute on Aging of the National Institutes of Health under Award Number U01AG068057. Also involved in the project are INI faculty members Neda Jahanshad and Lauren Salminen, as well as consortium manager Sophia Thomopoulos. — Zara Greenbaum