Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer's disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer's Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy. Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks. - Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders - Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer's Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke - Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders

With the advent of computational intelligence-based approaches, such as bio-inspired techniques, and the availability of clinical data from various complex experiments, medical consultants, researchers, neurologists, and oncologists, there is huge scope for CI-based applications in medical oncology and neurological disorders. This book focuses on interdisciplinary research in this field, bringing together medical practitioners dealing with neurological disorders and medical oncology along with CI investigators. The book collects high-quality original contributions, containing the latest developments or applications of practical use and value, presenting interdisciplinary research and review articles in the field of intelligent systems for computational oncology and neurological disorders. Drawing from work across computer science, physics, mathematics, medical science, psychology, cognitive science, oncology, and neurobiology among others, it combines theoretical, applied, computational, experimental, and clinical research. It will be of great interest to any neurology or oncology researchers focused on computational approaches.

This book explores the challenges involved in handling medical big data in the diagnosis of neurological disorders. It discusses how to optimally reduce the number of neuropsychological tests during the classification of these disorders by using feature selection methods based on the diagnostic information of enrolled subjects. The book includes key definitions/models and covers their applications in different types of signal/image processing for neurological disorder data. An extensive discussion on the possibility of enhancing the abilities of AI systems using the different data analysis is included. The book recollects several applicable basic preliminaries of the different AI networks and models, while also highlighting basic processes in image processing for various neurological disorders. It also reports on several applications to image processing and explores numerous topics concerning the role of big data analysis in addressing signal and image processing in various real-world scenarios involving neurological disorders. This cutting-edge book highlights the analysis of medical data, together with novel procedures and challenges for handling neurological signals and images. It will help engineers, researchers and software developers to understand the concepts and different models of AI and data analysis. To help readers gain a comprehensive grasp of the subject, it focuses on three key features: ● Presents outstanding concepts and models for using AI in clinical applications involving neurological disorders, with clear descriptions of image representation, feature extraction and selection. ● Highlights a range of techniques for evaluating the performance of proposed CAD systems for the diagnosis of neurological disorders. ● Examines various signal and image processing methods for efficient decision support systems. Soft computing, machine learning and optimization algorithms are also included to improve the CAD systems used.

Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.

Computational Intelligence and Deep Learning Methods for Neuro-rehabilitation Applications explores the different possibilities of providing AI based neuro-rehabilitation methods to treat neurological disorders. This book provides in-depth knowledge on the challenges and solutions associated with the different varieties of neuro-rehabilitation through the inclusion of case studies and real-time scenarios in different geographical locations. Beginning with an overview of neuro-rehabilitation applications, the book discusses the role of machine learning methods in brain function grading for adults with Mild Cognitive Impairment, Brain Computer Interface for post-stroke patients, developing assistive devices for paralytic patients, and cognitive treatment for spinal cord injuries. Topics also include AI-based video games to improve the brain performances in children with autism and ADHD, deep learning approaches and magnetoencephalography data for limb movement, EEG signal analysis, smart sensors, and the application of robotic concepts for gait control. - Incorporates artificial intelligence techniques into neuro-rehabilitation and presents novel ideas for this process - Provides in-depth case studies and state-of-the-art methods, along with the experimental study - Presents a block diagram based complete set-up in each chapter to help in real-time implementation

Over the past few years, there have been fundamental changes in the diagnosing and treating patients with chronic diseases, significantly affecting management of neurological movement disorders. In addition, the health and fitness sector developed several devices to better classify, track, and potentially treat chronic diseases. Both handling and interpreting these large datasets has been revolutionized, by machine and deep learning approaches, leading to new and more effective therapies, resulting in longer survival rates. Handbook of Digital Technologies in Movement Disorders aims to unite these factors to provide a comprehensive guide to patient focused treatments for movement disorders. This book is divided into five distinct sections, starting with an introduction to digital technologies, concepts, and terminologies. The following section reviews various perspectives on technology in movement disorders, including patient and medical professionals. The third section presents technologies used in detecting, measuring progression, and determining response to treatments. This is followed by reviewing the technology used in various treatments of movement disorders including assistive and robotic technologies. Finally, the last section examines the challenges with technology including privacy and other ethical issues. - Reviews different stakeholders' perspectives on technology in movement disorders - Presents technological advancements for diagnosing, monitoring, and managing Parkinson's disease - Discusses challenges with implementing technology into treatment

In the ancient world, health seekers traversed vast distances to pursue well-being. Fast forward to the 21st century, and the concept of medical tourism has evolved into a global industry worth billions of dollars. The burgeoning demand for quality healthcare has pushed traditional boundaries, necessitating a more sophisticated and interconnected healthcare ecosystem. Integrating Artificial Intelligence (AI) into medical practices empowers individuals to actively manage their health while providing healthcare practitioners with invaluable insights for tailored patient care. Impact of AI and Robotics on the Medical Tourism Industry delves into the transformative impact of AI and robotics on this dynamic sector. It explores how AI can encourage and assist patients in their health plans while augmenting healthcare practitioners' capabilities to utilize specific patient data to offer unique guidance. It dissects real-world examples, such as Cleveland Clinic Abu Dhabi's integration of robotic surgery, showcasing how these technologies revolutionize traditional medical practices.

This book gathers new empirical findings fostering advances in the areas of communication design and branding, with a special emphasis of interdisciplinary approaches showing how to combine knowledge in those fields to improve businesses in a digital, global world. Gathering original, peer-reviewed contributions written by designers, computer scientists, marketer and product managers, this book provides both the communication and branding communities with a timely snapshot of current strategies and best-practices to improve different kinds of business through design. By highlighting current challenges, it is also intended to inspire and foster collaboration between different groups, in both university and industry.

The book acts as a guide, taking the reader into the smart system domain and providing theoretical and practical knowledge along with case studies in smart healthcare. The book uses a blend of interdisciplinary approaches such as IoT, blockchain, augmented reality, and virtual reality for the implementation of cost-effective, real-time, and user-friendly solutions for healthcare problems. Immersive Virtual and Augmented Reality in Healthcare: An IoT and Blockchain Perspective presents the trends, best practices, techniques, developments, sensors, materials, and case studies that are using augmented and virtual reality environments with the state-of-the-art latest technologies like IoT, blockchain, and machine learning in the implementation of healthcare systems. The book focuses on the design and implementation of smart healthcare systems with major challenges to further explore more robust and efficient healthcare solutions in terms of low cost, faster algorithms, more sensitive IoT sensors, faster data communication, and real-time solutions for treatment. It discusses the use of virtual and augmented reality and how it can provide user-friendly and interactive communication within healthcare systems. Illustrated through case studies, the book conveys how different hospitals and healthcare equipment providers can adopt good practices found in the book to improve the performance/productivity of their staff and system. The content is rounded out by providing how IoT, blockchain, and artificial intelligence can provide the framework for designing and/or upgrading traditional healthcare systems by increasing security and data privacy. A valuable resource for engineers working with systems, the healthcare professionals involved in the design and development of healthcare devices and systems, researcher scholars, multidisciplinary scientists, students, and academics who are wishing to explore the use of virtual and augmented reality in new and existing healthcare systems.

Designing Exoskeletons focuses on developing exoskeletons, following the lifecycle of an exoskeleton from design to manufacture. It demonstrates how modern technologies can be used at every stage of the process, such as design methodologies, CAD/CAE/CAM software, rapid prototyping, test benches, materials, heat and surface treatments, and manufacturing processes. Several case studies are presented to provide detailed considerations on developing specific topics. Exoskeletons are designed to provide work-power, rehabilitation, and assistive training to sports and military applications. Beginning with a review of the history of exoskeletons from ancient to modern times, the book builds on this by mapping out recent innovations and state-of-the-art technologies that utilize advanced exoskeleton design. Presenting a comprehensive guide to computer design tools used by bioengineers, the book demonstrates the capabilities of modern software at all stages of the process, looking at computer-aided design, manufacturing, and engineering. It also details the materials used to create exoskeletons, notably steels, engineering polymers, composites, and emerging materials. Manufacturing processes, both conventional and unconventional are discussed—for example, casting, powder metallurgy, additive manufacturing, and heat and surface treatments. This book is essential reading for those in the field of exoskeletons, such as designers, workers in research and development, engineering and design students, and those interested in robotics applied to medical devices.