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Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effective management. AI-Driven Alzheimer's Disease Detection and Prediction offers a groundbreaking solution by leveraging advanced artificial intelligence (AI) techniques to enhance early diagnosis and prediction of AD. This edited book provides a comprehensive overview of state-of-the-art research, methodologies, and applications at the intersection of AI and AD detection. By bridging the gap between traditional diagnostic methods and cutting-edge technology, this book facilitates knowledge exchange, fosters interdisciplinary collaboration, and contributes to innovative solutions for AD management.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Artificial Intelligence and Big Data Analytics for Smart Healthcare serves as a key reference for practitioners and experts involved in healthcare as they strive to enhance the value added of healthcare and develop more sustainable healthcare systems. It brings together insights from emerging sophisticated information and communication technologies such as big data analytics, artificial intelligence, machine learning, data science, medical intelligence, and, by dwelling on their current and prospective applications, highlights managerial and policymaking challenges they may generate. The book is split into five sections: big data infrastructure, framework and design for smart healthcare; signal processing techniques for smart healthcare applications; business analytics (descriptive, diagnostic, predictive and prescriptive) for smart healthcare; emerging tools and techniques for smart healthcare; and challenges (security, privacy, and policy) in big data for smart healthcare. The content is carefully developed to be understandable to different members of healthcare chain to leverage collaborations with researchers and industry. - Presents a holistic discussion on the new landscape of data driven medical technologies including Big Data, Analytics, Artificial Intelligence, Machine Learning, and Precision Medicine - Discusses such technologies with case study driven approach with reference to real world application and systems, to make easier the understanding to the reader not familiar with them - Encompasses an international collaboration perspective, providing understandable knowledge to professionals involved with healthcare to leverage productive partnerships with technology developers
Diagnosis and Management in Dementia: The Neuroscience of Dementia, Volume 1 consolidates different fields of dementia into a single book, covering a range of subjects, including Alzheimer's disease, Lewy body dementia, mixed dementia, vascular dementia, physical activity, risk factors, mortality, biomarkers, SPECT, CT, MRI, questionnaires, nutrition, sleep, delirium, hearing loss, agitation, aggression, delusions, anxiety, depression, hallucinations, psychosis, senile plaques, tau and amyloid-beta, neuroinflammation, molecular biology, and more. With an impact on millions globally, and billions of research dollars being invested in dementia research, this book will stimulate research in the area and inform researchers. - Offers comprehensive coverage of a broad range of topics related to dementia - Serves as a foundational collection for neuroscientists and neurologists on the biology of dementia and brain dysfunction - Contains in each chapter an abstract, key facts, mini dictionary of terms, and summary points to aid in understanding - Provides unique sections on specific subareas, intellectual components, and knowledge-based niches that will help readers navigate key areas for research and further clinical recommendations - Features preclinical and clinical studies to help researchers map out key areas for research and further clinical recommendations - Serves as a "one-stop" source for everything you need to know about dementia
This book constitutes the proceedings of the Second International Workshop on Predictive Intelligence in Medicine, PRIME 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 18 papers presented in this volume were carefully reviewed and selected for inclusion in this book. The contributions describe new cutting-edge predictive models and methods that solve challenging problems in the medical field for a high-precision predictive medicine.
This book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, on September 4 and 5, 2020. The book starts by addressing the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, and speech and gesture analysis have been included with upfront details. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber physical system and smart agriculture. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.
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.
These are the proceedings of the International Conference on ISMAC-CVB, held in Palladam, India, in May 2018. The book focuses on research to design new analysis paradigms and computational solutions for quantification of information provided by object recognition, scene understanding of computer vision and different algorithms like convolutional neural networks to allow computers to recognize and detect objects in images with unprecedented accuracy and to even understand the relationships between them. The proceedings treat the convergence of ISMAC in Computational Vision and Bioengineering technology and includes ideas and techniques like 3D sensing, human visual perception, scene understanding, human motion detection and analysis, visualization and graphical data presentation and a very wide range of sensor modalities in terms of surveillance, wearable applications, home automation etc. ISMAC-CVB is a forum for leading academic scientists, researchers and research scholars to exchange and share their experiences and research results about all aspects of computational vision and bioengineering.
This book constitutes the refereed proceedings of the 13th International Conference on Brain Informatics, BI 2020, held in Padua, Italy, in September 2020. The conference was held virtually due to the COVID-19 pandemic. The 33 full papers were carefully reviewed and selected from 57 submissions. The papers are organized in the following topical sections: cognitive and computational foundations of brain science; investigations of human information processing systems; brain big data analytics, curation and management; informatics paradigms for brain and mental health research; and brain-machine intelligence and brain-inspired computing.
This book covers wide areas of animal and human psychopharmacology with clinical utility in the treatment of psychiatric and neurological (e.g Alzheimer's disease) disorders. The main theme is to develop a new paradigm for drug discovery that questions the claim that animal models or assays fail adequately to predict Phase 3 clinical trials. A new paradigm is advocated that stresses the importance of intermediate staging points between these extremes that depend on suitable translation of findings from animal studies to Phase 1 or Phase 2 studies utilising experimental medicine.