Drs. Leonard Scinto and Kirk Daffner provide a comprehensive survey of new diagnostic approaches to Alzheimer's disease. The authoritative contributors critically survey the most promising current research on early diagnostic markers for Alzheimer's disease, including the elucidation of changes in the brain revealed by structural and functional neuroimaging, as well as the characteristic patterns of cognitive decline that are documented by sensitive neuropsychological tests, various genetic markers, and biological assays. Early Diagnosis of Alzheimer's Disease illuminates the complex issues surrounding the search for early markers of this increasingly widespread disease. It will establish a new standard reference guide for all those working with Alzheimer's patients.

Cognitive impairment, through Alzheimer’s disease or other related forms of dementia, is a serious concern for afflicted individuals and their caregivers. Understanding patients’ mental states and combatting social stigmas are important considerations in caring for cognitively impaired individuals. Technology is playing an increasing role in the lives of the elderly. One of the most prevalent developments for the aging population is the use of technological innovations for intervention and treatment of individuals with mental impairments. Research Anthology on Diagnosing and Treating Neurocognitive Disorders examines the treatment, diagnosis, prevention, and therapeutic and technological interventions of neurodegenerative disorders. It also describes programs and strategies that professional and family caregivers can implement to engage and improve the quality of life of persons suffering from cognitive impairment. Highlighting a range of topics such as dementia, subjective wellbeing, and cognitive decline, this publication is an ideal reference source for speech pathologists, social workers, occupational therapists, psychologists, psychiatrists, neurologists, pediatricians, researchers, clinicians, and academicians seeking coverage on neurocognitive disorder identification and strategies for clinician support and therapies.

Aging is an inevitable part of life and is becoming a worldwide social, economic and health problem. This is mainly due to the fact that the increasing proportion of individuals in the advanced age category have a higher probability of developing age-related disorders, such as type II diabetes mellitus, cardiovascular disorders, sarcopenia, and neurodegenerative conditions. New therapeutic approaches are still needed to decrease or slow the effects of such diseases. Advances in -omic technologies, such as genomics, transcriptomics, proteomics and metabolomics, have significantly advanced our understanding of disease in multiple medical areas, as the analysis of multiple molecular networks has simultaneously provided a more integrated view of disease pathways. It is hoped that emerging hits from these analyses might be prioritized for further screening as potential novel drug targets for increasing the human healthspan in line with the lifespan. In turn, this will lead to new therapeutic strategies as well as drug development projects by the pharmaceutical industry. This book presents a series of reviews describing studies that have resulted in identification of new potential drug targets for age-related disorders. Much of this information has come from -omic comparisons of healthy and disease states or from testing the effects of new therapeutic approaches. Authored by experts from around the globe, each chapter is presented in the context of specific chronic diseases or therapeutic strategies. This book is designed for researchers in the areas of aging and chronic disease, as well as clinical scientists, physicians and stakeholders in major drug companies.

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.

This book explores international biomedical research and development on the early diagnosis of Alzheimer's disease. It offers timely, multidisciplinary reflections on the social and ethical issues raised by promises of early diagnostics and asks under which conditions emerging diagnostic technologies can be considered a responsible innovation. The initial chapters in this edited volume provide an overview and a critical discussion of recent developments in biomedical research on Alzheimer's disease. Subsequent contributions explore the values at stake in current practices of dealing with Alzheimer's disease and dementia, both within and outside the biomedical domain. Novel diagnostic technologies for Alzheimer's disease emerge in a complex and shifting field, full of controversies. Innovating with care requires a precise mapping of how concepts, values and responsibilities are filled in through the confrontation of practices. In doing so, the volume offers a practice-based approach of responsible innovation that is also applicable to other fields of innovation.

Biomarkers in Alzheimer's Disease provides a comprehensive overview of all modalities of Alzheimer's disease biomarkers, including neuroimaging, cerebrospinal fluid, genomic, and peripheral systems. Each chapter integrates molecular/cellular abnormality due to Alzheimer's disease and technological advancement of biomarkers techniques. The book is ideal for clinical neuroscience and molecular/cellular neuroscience researchers, psychiatrists, and allied healthcare practitioners involved in the diagnosis and management of patients with cognitive impairment and Alzheimer's disease, and for differential diagnosis of Alzheimer's disease with other non-Alzheimer's dementia. - Presents a comprehensive overview detailing all modalities of Alzheimer's disease biomarkers - Written for neuroscience researchers and clinicians studying or treating patients with Alzheimer's Disease - Integrates, in each chapter, the molecular/cellular abnormality due to Alzheimer's disease and the technological advancement of biomarkers techniques

A definitive and compelling book on one of today's most prevalent illnesses. In 2020, an estimated 5.8 million Americans had Alzheimer’s, and more than half a million died because of the disease and its devastating complications. 16 million caregivers are responsible for paying as much as half of the $226 billion annual costs of their care. As more people live beyond their seventies and eighties, the number of patients will rise to an estimated 13.8 million by 2050. Part case studies, part meditation on the past, present and future of the disease, The Problem of Alzheimer's traces Alzheimer’s from its beginnings to its recognition as a crisis. While it is an unambiguous account of decades of missed opportunities and our health care systems’ failures to take action, it tells the story of the biomedical breakthroughs that may allow Alzheimer’s to finally be prevented and treated by medicine and also presents an argument for how we can live with dementia: the ways patients can reclaim their autonomy and redefine their sense of self, how families can support their loved ones, and the innovative reforms we can make as a society that would give caregivers and patients better quality of life. Rich in science, history, and characters, The Problem of Alzheimer's takes us inside laboratories, patients' homes, caregivers’ support groups, progressive care communities, and Jason Karlawish's own practice at the Penn Memory Center.

This book constitutes the refereed proceedings of the workshop held in conjunction with the 28th International Conference on Artificial Intelligence, IJCAI 2019, held in Macao, China, in August 2019: the First International Workshop on Human Brain and Artificial Intelligence, HBAI 2019. The 24 full papers presented were carefully reviewed and selected from 62 submissions. The papers are organized according to the following topical headings: computational brain science and its applications; brain-inspired artificial intelligence and its applications.

The book covers the most recent developments in machine learning, signal analysis, and their applications. It covers the topics of machine intelligence such as: deep learning, soft computing approaches, support vector machines (SVMs), least square SVMs (LSSVMs) and their variants; and covers the topics of signal analysis such as: biomedical signals including electroencephalogram (EEG), magnetoencephalography (MEG), electrocardiogram (ECG) and electromyogram (EMG) as well as other signals such as speech signals, communication signals, vibration signals, image, and video. Further, it analyzes normal and abnormal categories of real-world signals, for example normal and epileptic EEG signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing.

Dr Daniel Gibbs is one of 50 million people worldwide with an Alzheimer's disease diagnosis. Unlike most patients with Alzheimer's, however, Dr Gibbs worked as a neurologist for twenty-five years, caring for patients with the very disease now affecting him. Also unusual is that Dr Gibbs had begun to suspect he had Alzheimer's several years before any official diagnosis could be made. Forewarned by genetic testing showing he carried alleles that increased the risk of developing the disease, he noticed symptoms of mild cognitive impairment long before any tests would have alerted him. In this highly personal account, Dr Gibbs documents the effect his diagnosis has had on his life and explains his advocacy for improving early recognition of Alzheimer's. Weaving clinical knowledge from decades caring for dementia patients with his personal experience of the disease, this is an optimistic tale of one man's journey with early-stage Alzheimer's disease. Soon to be a documentary film on MTV/Paramount +.