When it comes to Parkinson's disease, one of the most important issues revolves around early detection and accurate diagnosis. The intricacies of this neurodegenerative disorder often elude timely identification, leaving patients and healthcare providers grappling with its progressive symptoms. Ethical concerns surrounding the use of machine learning to aid in diagnosis further complicate this challenge. This issue is particularly significant for research scholars, PhD fellows, post-doc fellows, and medical and biomedical scholars seeking to unravel the mysteries of Parkinson's disease and develop more effective treatments. Intelligent Technologies and Parkinson’s Disease: Prediction and Diagnosis serves as a beacon of hope in the quest to revolutionize Parkinson's disease diagnosis and treatment. It unveils the remarkable potential of artificial intelligence (AI) and machine learning (ML) in remodeling the way we approach this debilitating condition. With a comprehensive exploration of AI's capacity to analyze speech patterns, brain imaging data, and gait patterns, this book offers a powerful solution to the challenges of early detection and accurate diagnosis.

Artificial Intelligence (AI) in general and machine learning (ML) and deep learning (DL) in particular and related digital technologies are a couple of fledging paradigms that next-generation healthcare services are sprinting towards. These digital technologies can transform various aspects of healthcare, leveraging advances in computing and communication power. With a new spectrum of business opportunities, AI-powered healthcare services will improve the lives of patients, their families, and societies. However, the application of AI in the healthcare field requires special attention given the direct implication with human life and well-being. Rapid progress in AI leads to the possibility of exploiting healthcare data for designing practical tools for automated diagnosis of chronic diseases such as dementia and diabetes. This book highlights the current research trends in applying AI models in various disease diagnoses and prognoses to provide enhanced healthcare solutions. The primary audience of the book are postgraduate students and researchers in the broad domain of healthcare technologies. Features In-depth coverage of the role of AI in smart healthcare Research guidelines for AI and data science researchers/practitioners interested in the healthcare sector Comprehensive coverage on security and privacy issues for AI in smart healthcare

One of the main benefits of this book is that it presents a comprehensive and innovative eHealth framework that leverages deep learning and IoT wearable devices for the evaluation of Parkinson's disease patients. This framework offers a new way to assess and monitor patients' motor deficits in a personalized and automated way, improving the efficiency and accuracy of diagnosis and treatment. Compared to other books on eHealth and Parkinson's disease, this book offers a unique perspective and solution to the challenges facing patients and healthcare providers. It combines state-of-the-art technology, such as wearable devices and deep learning algorithms, with clinical expertise to develop a personalized and efficient evaluation framework for Parkinson's disease patients. This book provides a roadmap for the integration of cutting-edge technology into clinical practice, paving the way for more effective and patient-centered healthcare. To understand this book, readers should have a basic knowledge of eHealth, IoT, deep learning, and Parkinson's disease. However, the book provides clear explanations and examples to make the content accessible to a wider audience, including researchers, practitioners, and students interested in the intersection of technology and healthcare.

Cognitive disorders are a growing concern, affecting individuals across the age spectrum and society. These disorders can profoundly disrupt daily life, and their timely diagnosis is crucial for effective intervention and care. As the prevalence of cognitive disorders continues to rise, the need for precise and early diagnosis has never been more pressing. Intelligent Solutions for Cognitive Disorders is a research-based book which delves into the intersection of medical science and technology, exploring the latest advancements in cognitive disorder diagnosis and treatment. This book assembles a multidisciplinary team of experts, including researchers, clinicians, and technologists, to address this challenge head-on. This book commences with an in-depth introduction to cognitive disorders, providing a solid foundation for readers of all backgrounds. It then navigates the role of intelligent systems in cognitive healthcare, unveiling the potential of artificial intelligence, machine learning, and deep learning techniques. The book highlights how these intelligent systems can enable the early and accurate detection of cognitive disorders, a pivotal factor in improving patients' quality of life. This book is an invaluable resource for technologists, researchers, linguists, data scientists, healthcare practitioners, medical professionals, and students seeking a comprehensive understanding of cognitive disorders and the role of intelligent technologies in their diagnosis and care.

Stemming from environmental, genetic, and situational factors, chronic disease is a critical concern in modern medicine. Managing treatment and controlling symptoms is imperative to the longevity and quality of life of patients with such diseases. The Handbook of Research on Trends in the Diagnosis and Treatment of Chronic Conditions features current research on the diagnosis, monitoring, management, and treatment of recurring diseases such as diabetes, Parkinson's disease, autoimmune disorders, and others. This handbook is intended for practitioners and researchers across various disciplines including, but not limited to, biology, biomedical engineering, computer science, and information and communication technologies. Aimed at identifying new disease determinants and the way in which new technologies can contribute to improved health outcomes, this handbook covers a variety of topics, including wearable and mobile technologies, capillaroscopy imaging, diagnostic and monitoring methods, and disease prediction modeling, among others.

As there are no proper medical tests available to predict certain diseases such as Alzheimer’s and Parkinson’s at an early stage, there is a need to further study and consider the potential uses of bio- and nature-inspired algorithms and future technologies such as machine learning in correlation to disease detection and treatment. Bio-Inspired Algorithms and Devices for Treatment of Cognitive Diseases Using Future Technologies considers new tools for early detection of cognitive brain diseases using devices and algorithms whose basic concept is taken from nature and discusses design, analysis, and application of various bionics or bio-inspired algorithms. Covering topics such as depression and cognitive science, this publication is an ideal resource for researchers, academicians, industry professionals, psychologists, psychiatrists, nurses, engineers, instructors, and students.

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

Academic scholars and professionals are currently grappling with hurdles in optimizing diagnostic processes, as traditional methodologies prove insufficient in managing the intricate and voluminous nature of medical data. The diverse range of imaging techniques, spanning from endoscopy to magnetic resonance imaging, necessitates a more unified and efficient approach. This complexity has created a pressing need for streamlined methodologies and innovative solutions. Academic scholars find themselves at the forefront of addressing these challenges, seeking ways to leverage AI's full potential in improving the accuracy of medical imaging diagnostics and, consequently, enhancing overall patient outcomes. Future of AI in Medical Imaging, stands as a solution to the challenges faced by academic scholars in the realm of medical imaging. The book lays a solid groundwork for understanding the complexities of medical imaging systems. Through an exploration of various imaging modalities, it not only addresses the current issues but also serves as a guide for scholars to navigate the landscape of AI-integrated medical diagnostics. This collaborative effort not only illuminates the existing hurdles of medical imaging but also looks towards a future where AI-driven diagnostics and personalized medicine become indispensable tools, significantly elevating patient outcomes.