Early Detection In Alzheimers Disease Biomarkers And Machines

Early Detection of Alzheimer’s Disease: Biological and Technological Advances aims to introduce to a wide audience the high global priority problem of detecting AD prior to dementia onset. According to the Alzheimer’s Association, 5.8 million Americans are living with Alzheimer’s and care costs will cost the nation approximately $290 billion (2019 Alzheimer’s Disease Facts and Figures). With the failure of recent AD drug trials, many hypothesize that by the time symptoms appear, it is too late to be treated. Early detection can offer benefits such as more choice of medications, ability to participate in clinical trials, more time for family and for care planning. This book outlines potential solutions to the above problem using opportunities arising from the technology revolution, advances in neuroscience, and molecular biology. Most importantly, it discusses a paradigm shift from a reactive to a proactive diagnostic approach, aiming to detect disease before occurrence of symptoms. Topics covered include the use of sensing technologies (e.g. smartphones, smartwatches, Internet of Things) to detect early disease-related changes, the application of data science (machine learning/AI) to extract otherwise invisible disease features from these datasets and the potential to personalize diagnosis based on tracking changes in individual behaviours. Advances in blood-based biomarkers, brain imaging, and the potential for early diagnosis to aid interventions (lifestyle, dietary, pharmacological) to delay future development of dementia are also discussed. Outlines the importance of early diagnosis of Alzheimer’s Disease Helps readers understand the limitations of current clinical approaches and the need for a paradigmatic shift in diagnostic practice Discusses the potential role of technology in clinical practice using machine learning and artificial intelligence and the potential to personize diagnosis and treatment

In view of the growing prevalence of AD worldwide, there is an urgent need for the development of better diagnostic tools and more effective therapeutic interventions. Indeed, much work in this field has been done during last decades. As such, a major goal of current clinical research in AD is to improve early detection of disease and presymptomatic detection of neuronal dysfunction, concurrently with the development of better tools to assess disease progression in this group of disorders. All these putative correlates are commonly referred to as AD-related biomarkers. The ideal biomarker should be easy to quantify and measure, reproducible, not subject to wide variation in the general population and unaffected by co- morbid factors. For evaluation of therapies, a biomarker needs to change linearly with disease progression and closely correlate with established clinico-pathological parameters of the disease. There is growing evidence that the use of biomarkers will increase our ability to better indentify the underlying biology of AD, especially in its early stages. These biomarkers will improve the detection of the patients suitable for research studies and drug trials, and they will contribute to a better management of the disease in the clinical practice. Indeed, much work in this field has been done during last decades. The vast number of important applications, combined with the untamed diversity of already identified biomarkers, show that there is a pressing need to structure the research made on AD biomarkers into a solid, comprehensive and easy to use tool to de deployed in clinical settings. To date there are few publications compiling results on this topic. That is why when I was asked to address this task I accepted inmediately. I am happy to present you a bundle of the best articles published about biomarkers for Alzheimer’s disease in recent times.

This issue of Medical Clinics covers the important advances surrounding the early intervention of Alzheimer's Disease. Guest edited by world-renowned experts from around the world, the topics covered will include evaluation of new diagnostic criteria, CSF biomarkers, diagnostic imaging, neurocognitive approaches, breaking clinical trials and more.

Alzheimer's disease (AD) is characterized by the deposition into the brain of amyloid peptide, which originates a cascade of inflammatory events leading eventually to neuronal death. These pathological events likely occur several years before the clinical manifestation of the disease, implying that potential therapeutical interventions are currently started too late to give beneficial results. Recently, growing attention has been paid to the Mild Cognitive Impairment (MCI), considered the prodromal phase of AD, as 80% of subjects with MCI have been shown to develop AD within 5 years. Several studies aimed to identify biological markers to differentiate between normal aging and incipient AD have been carried out, including cerebrospinal fluid (CSF) analysis and neuroimaging. In particular, the evaluation of CSF Amyloid beta (1-42) levels, together with tau and phospotau, are of help for recognizing early AD. Besides, a number of additional molecules are altered in CSF. Other early modifications have been observed in peripheral cells, such as fibroblasts and leukocytes, as well as in serum from patients. Biomarkers for AD represent important tools supporting the clinical diagnosis and the choice of potential therapeutic options. Moreover, they would be of great help for the selection of cohorts of homogeneous patients for clinical trials with new disease-modifying compounds. This book is aimed to give an update about MCI as prodromal Alzheimer's disease (AD), to discuss the main known pathological mechanisms at the basis of AD and to describe the possible biomarkers to be used for an early diagnosis of the disease.

This volume discusses the importance of imaging, fluid, and genetic biomarkers in detecting the earliest pre-symptomatic stages of Alzheimer’s disease (AD). The chapters in this book are separated into five parts. Part one explores the reasons why we need improved approaches for early detection and diagnosis of AD. Part two describes clinical and research techniques for preclinical AD diagnosis. Part three looks at the current methods in use to diagnose AD and how they can also be used for early detection in healthy older individuals. Parts four and five talk about technological advancements in AD diagnosis and ethical considerations in AD research. In Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory. Comprehensive and cutting-edge, Biomarkers for Preclinical Alzheimer’s Disease is a valuable resource for individuals interested in the use of biomarker strategies to improve early AD detection. This book will be of great interest to researchers and clinicians from both academia and industry.”

This issue of Medical Clinics covers the important advances surrounding the early intervention of Alzheimer's Disease. Guest edited by world-renowned experts from around the world, the topics covered will include evaluation of new diagnostic criteria, CSF biomarkers, diagnostic imaging, neurocognitive approaches, breaking clinical trials and more.

This up-to-date, superbly illustrated book is a practical guide to the effective use of neuroimaging in the patient with cognitive decline. It sets out the key clinical and imaging features of the various causes of dementia and directs the reader from clinical presentation to neuroimaging and on to an accurate diagnosis whenever possible. After an introductory chapter on the clinical background, the available "toolbox" of structural and functional neuroimaging techniques is reviewed in detail, including CT, MRI and advanced MR techniques, SPECT and PET, and image analysis methods. The imaging findings in normal ageing are then discussed, followed by a series of chapters that carefully present and analyze the key findings in patients with dementias. Throughout, a practical approach is adopted, geared specifically to the needs of clinicians (neurologists, radiologists, psychiatrists, geriatricians) working in the field of dementia, for whom this book will prove an invaluable resource.

The book details deep learning models like ANN, RNN, LSTM, in many industrial sectors such as transportation, healthcare, military, agriculture, with valid and effective results, which will help researchers find solutions to their deep learning research problems. We have entered the era of smart world devices, where robots or machines are being used in most applications to solve real-world problems. These smart machines/devices reduce the burden on doctors, which in turn make their lives easier and the lives of their patients better, thereby increasing patient longevity, which is the ultimate goal of computer vision. Therefore, the goal in writing this book is to attempt to provide complete information on reliable deep learning models required for e-healthcare applications. Ways in which deep learning can enhance healthcare images or text data for making useful decisions are discussed. Also presented are reliable deep learning models, such as neural networks, convolutional neural networks, backpropagation, and recurrent neural networks, which are increasingly being used in medical image processing, including for colorization of black and white X-ray images, automatic machine translation images, object classification in photographs/images (CT scans), character or useful generation (ECG), image caption generation, etc. Hence, reliable deep learning methods for the perception or production of better results are a necessity for highly effective e-healthcare applications. Currently, the most difficult data-related problem that needs to be solved concerns the rapid increase of data occurring each day via billions of smart devices. To address the growing amount of data in healthcare applications, challenges such as not having standard tools, efficient algorithms, and a sufficient number of skilled data scientists need to be overcome. Hence, there is growing interest in investigating deep learning models and their use in e-healthcare applications. Audience Researchers in artificial intelligence, big data, computer science, and electronic engineering, as well as industry engineers in transportation, healthcare, biomedicine, military, agriculture.