Deep Learning for Medical Applications with Unique Data informs readers about the most recent deep learning-based medical applications in which only unique data gathered in real cases are used. The book provides examples of how deep learning can be used in different problem areas and frameworks in both clinical and research settings, including medical image analysis, medical image registration, time series analysis, medical data synthesis, drug discovery, and pre-processing operations. The volume discusses not only positive findings, but also negative ones obtained by deep learning techniques, including the use of newly developed deep learning techniques rarely reported in the existing literature. The book excludes research works with ready data sets and includes only unique data use to better understand the state of deep learning in real-world cases, along with the feedback and user experiences from physicians and medical staff for applied deep learning-based solutions. Other applications presented in the book include hybrid solutions with deep learning support, disease diagnosis with deep learning focusing on rare diseases and cancer, patient care and treatment, genomics research, as well as research on robotics and autonomous systems.

Big Data permeates all aspects of modern life, and while there is no shortage of potential benefits resulting from this, author Henrik Skaug Saetra argues that we must also understand the threats Big Data poses to liberty. The issues discussed in Big Data's Threat to Liberty: Surveillance, Nudging, and the Curation of Information are related to how we are constantly under surveillance. Data is gathered from our homes, our cars, our smartphones, various devices around the house, and public sources such as facial recognition enabled camera surveillance and various websites and social networks. Furthermore, the information gathered is used to influence our actions. Detailed personality profiles are utilized in order to make us purchase products and services, or pay our taxes, through tailor-made nudges aimed at irrational and subconscious mechanisms, and delivered with a level of precision only possible with Big Data-driven algorithmic curation of data. Finally, the information we receive through various media is curated by algorithms, and even people are curated in order to satisfy our desires. By providing us with what the algorithm believes we want, we are spared from the exposure of unpleasant information, and even unpleasant people. The ideological landscapes we traverse are thus characterized by conformity, and a concomitant tyranny of popular opinion becomes ever more coercive as this occurs. The question is: How does being constantly watched, manipulated, and having our world-views shaped as just described affect our freedom? In this book it is argued that Big Data's threat to individual liberty is routinely misunderstood and underappreciated due to (a) vagueness resulting from the concept of liberty being used without it being defined, or (b) the use of definitions based on flawed understandings of what liberty is. In this new and unique contribution to the ethics of Big Data and artificial intelligence, both these challenges are thoroughly addressed.

The third edition of Joint Replacement Technology provides a thoroughly updated review of recent developments in joint replacement technology. Joint replacement is a standard treatment for joint degradation and has improved the quality of life of millions of patients. Collaboration between clinicians and researchers is critical to its continued success and to meet the rising expectations of patients and surgeons. This edition covers a range of updated and new content, ranging from chapters on materials analysis and selection, to methodologies and techniques used for joint replacement and clinical challenges of replacing specific joints. Key topics include tribological considerations and experiments; challenges in joint bearing surfaces; cementless fixation techniques; healing responses to implants. Clinical challenges and perspectives are covered with the aid of case studies. Thanks to its widespread collaboration and international contributors, Joint Replacement Technology, Third Edition is useful for materials scientists and engineers in both academia and the biomedical industry. Chemists, clinicians, and other researchers in this area will also find this text invaluable.

Tailor-Made and Functionalized Biopolymer Systems: For Drug Delivery and Biomedical Applications covers the design and application of these functionalized and tailor-made biopolymers and biopolymer systems intended for drug delivery and biomedical applications. Various concepts, design protocols and biomedical applications of tailor-made biopolymer systems are covered, guiding the reader from theoretical knowledge to practical application. Authored by an array of experts from global institutions, this book offers an interdisciplinary approach to how tailor-made biopolymers lead to novel drug delivery and treatment solutions. This will be a useful reference to a broad audience, including biomedical engineers, materials scientists, pharmacologists and chemists.

Leveraging Artificial Intelligence in Global Epidemics provides readers with a detailed technical description of the role Artificial Intelligence plays in various stages of a disease outbreak, using COVID-19 as a case study. In the fight against epidemics, medical staff are on the front line; but behind the lines the battle is fought by researchers, and data scientists. Artificial Intelligence has been helping researchers with computer modeling and simulation for predictions about disease progression, the overall economic situation, tax incomes and population development. In the same manner, AI can prepare researchers for any emergency situation by backing the medical science. Artificial Intelligence plays a key and cutting-edge role in the preparedness for and dealing with the outbreak of global epidemics. It can help researchers analyze global data about known viruses to predict the patterns of the next pandemic and the impacts it will have. Not only prediction, AI plays an increasingly important role in assessing readiness, early detection, identification of patients, generating recommendations, situation awareness and more. It is up to the right input and the innovative ways by humans to leverage what AI can do. As COVID-19 has grabbed the world and its economy today, an analysis of the COVID-19 outbreak and the global responses and analytics will pay a long way in preparing humanity for such future situations.

Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible.

Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body. The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body. Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB (R) and IBM Watson (R).

In recent years, scientific research and translation medicine have placed increased emphasis on computational methodology and data curation across many disciplines, both to advance underlying science and to instantiate precision-medicine protocols in the lab and in clinical practice. The nexus of concerns related to oncology, cardiology, and virology (SARS-CoV-2) presents a fortuitous context within which to examine the theory and practice of biomedical data curation. Innovative Data Integration and Conceptual Space Modeling for COVID, Cancer, and Cardiac Care argues that a well-rounded approach to data modeling should optimally embrace multiple perspectives inasmuch as data-modeling is neither a purely formal nor a purely conceptual discipline, but rather a hybrid of both. On the one hand, data models are designed for use by computer software components, and are, consequently, constrained by the mechanistic demands of software environments; data modeling strategies must accept the formal rigors imposed by unambiguous data-sharing and query-evaluation logic. In particular, data models are not well-suited for software-level deployment if such models do not translate seamlessly to clear strategies for querying data and ensuring data integrity as information is moved across multiple points. On the other hand, data modeling is, likewise, constrained by human conceptual tendencies, because the information which is managed by databases and data networks is ultimately intended to be visualized/utilized by humans as the end-user. Thus, at the intersection of both formal and humanistic methodology, data modeling takes on elements of both logico-mathematical frameworks (e.g., type systems and graph theory) and conceptual/philosophical paradigms (e.g., linguistics and cognitive science). The authors embrace this two-sided aspect of data models by seeking non-reductionistic points of convergence between formal and humanistic/conceptual viewpoints, and by leveraging biomedical contexts (viz., COVID, Cancer, and Cardiac Care) so as to provide motivating examples and case-studies in this volume.

Long-Acting Drug Delivery Systems: Pharmaceutical, Clinical, and Regulatory Aspects offers a comprehensive overview of the technical, clinical, regulatory and industrial perspectives on these drug delivery systems. The book follows a sequential order, beginning with the current technical state-of-the-field and moving on to more clinical, industrial and regulatory topics. Opening chapters describe the current needs and potential applications of implantable and long-acting therapeutic approaches. The book goes on to describe established and novel long-acting systems, with a focus on the materials used to prepare these systems and their biocompatibility. Importantly, applied topics such as scale-up manufacturing, products under clinical trials and regulatory aspects are covered, offering the reader a holistic view of this rapidly growing field.

Green Biocomposites for Biomedical Engineering: Design, Properties, and Applications combines emergent research outcomes with fundamental theoretical concepts relevant to processing, properties and applications of advanced green composites in the field of biomedical engineering. The book outlines the design elements and characterization of biocomposites, highlighting each class of biocomposite separately. A broad range of biomedical applications for biocomposites is then covered, with a final section discussing the ethics and safety regulations associated with manufacturing and the use of biocomposites. With contributions from eminent editors and recognized authors around the world, this book is a vital reference for researchers in biomedical engineering, materials science and environmental science, both in industry and academia.

State of the Art on Grammatical Inference Using Evolutionary Method presents an approach for grammatical inference (GI) using evolutionary algorithms. Grammatical inference deals with the standard learning procedure to acquire grammars based on evidence about the language. It has been extensively studied due to its high importance in various fields of engineering and science. The book's prime purpose is to enhance the current state-of-the-art of grammatical inference methods and present new evolutionary algorithms-based approaches for context free grammar induction. The book's focus lies in the development of robust genetic algorithms for context free grammar induction. The new algorithms discussed in this book incorporate Boolean-based operators during offspring generation within the execution of the genetic algorithm. Hence, the user has no limitation on utilizing the evolutionary methods for grammatical inference.

Plant and Algal Hydrogels for Drug Delivery and Regenerative Medicine offers a materials-focused and systematic overview of biopolymeric hydrogels utilized for biomedical applications. The book details the synthesis and characterization of plant and algal-based hydrogels, with each chapter addressing a separate polysaccharide hydrogel type. Specific applications in drug delivery and regenerative medicine are also discussed, highlighting the efficacy, biocompatibility, benefits and challenges for each polysaccharide hydrogel subtype. There is increasing demand for biomaterials which reduce/prevent the host response, inflammation and rejection, hence this book provides a timely resource. Biopolymeric hydrogels have skyrocketed because of their necessity in in vivo applications. They create an environment similar to living tissue, which is both biocompatible and biodegradable. Plant and algal polysaccharides in particular are well-equipped with functional groups that are easily modified for beneficial results.

Real-Time Data Acquisition in Human Physiology: Real-Time Acquisition, Processing, and Interpretation-A MATLAB-Based Approach focuses on the design and development of a computer-based system to detect and digitally process human ECG, EMG, and carotid pulse waveforms in real time. The indigenous system developed and described in this book allows for an easy-to-interface, simple hardware arrangement for bio-signal detection. The computational functionality of MATLAB is verified for viewing, digital filtration, and feature extraction of acquired bio-signals. This book demonstrates a method of providing a relatively cost-effective solution to human physiology real-time monitoring, processing, and interpretation that is more realizable and would directly benefit a larger population of patients.

Storage Systems: Organization, Performance, Coding, Reliability and Their Data Processing was motivated by the 1988 Redundant Array of Inexpensive/Independent Disks proposal to replace large form factor mainframe disks with an array of commodity disks. Disk loads are balanced by striping data into strips-with one strip per disk- and storage reliability is enhanced via replication or erasure coding, which at best dedicates k strips per stripe to tolerate k disk failures. Flash memories have resulted in a paradigm shift with Solid State Drives (SSDs) replacing Hard Disk Drives (HDDs) for high performance applications. RAID and Flash have resulted in the emergence of new storage companies, namely EMC, NetApp, SanDisk, and Purestorage, and a multibillion-dollar storage market. Key new conferences and publications are reviewed in this book. The goal of the book is to expose students, researchers, and IT professionals to the more important developments in storage systems, while covering the evolution of storage technologies, traditional and novel databases, and novel sources of data. We describe several prototypes: FAWN at CMU, RAMCloud at Stanford, and Lightstore at MIT; Oracle's Exadata, AWS' Aurora, Alibaba's PolarDB, Fungible Data Center; and author's paper designs for cloud storage, namely heterogeneous disk arrays and hierarchical RAID.

Introduction to Biomedical Instrumentation and Its Applications delivers a detailed overview of the various instruments used in the biomedical and healthcare domain, focusing on both their main features and their uses in the medical industry. Each chapter focuses on biomedical instrumentation in a different medical discipline, covering a range of different topics including radiological devices, instruments used for blood analysis, defibrillators, ventilators, nerve stimulators and baby incubators. This book seeks to provide the reader with in-depth knowledge on biomedical devices, thus enabling them to contribute to the future development of instruments in the healthcare domain. This is a concise handbook that will be useful to students, researchers and practitioners involved in biomedical engineering, as well as doctors and clinicians who specialize in areas such as cardiology, anesthesiology and physiotherapy.

Titanium Alloys for Biomedical Development and Applications: Design, Microstructure, Properties and Application systematically introduces basic theories and progress in the research of biomedical ss-Ti alloys achieved by researchers from different fields. It focuses on a high-strength and low elastic modulus biomedical ss-Ti alloy (TLM), etc. designed by the authors. The alloy design methods, microstructural characteristics, mechanical properties, surface treatment methods and biocompatibility of the TLM alloy are discussed in detail, along with a concise description of the medical devices made from this alloy and the application examples. This book will appeal to researchers as well as students from different disciplines, including materials science, biology, medicine and engineering fields.

Biomedical Applications of Electrospinning and Electrospraying describes the principles and laboratory set up for electrospinning and electrospraying, addressing a range of biomedical applications. Sections cover novel combinational approaches, such as electrospinning/spraying and 3D printing. Electrospinning has evolved from being a technique to prepare random networks of textile fibers to a technique to fabricate highly ordered patterns of biomedical materials of defined scale. The technological advancements in recent years with regard to the way the jet is facilitated, how the jet path is controlled, and how the fibers are collected have provided invaluable insights into controlled fabrication of a material of choice. Additionally, the electrospray technique has also evolved from being a technique to prepare food formulations to a technique to prepare cell encapsulated beads for transplantation in clinics. Several innovations in this line, such as those leading to core-shell materials have tremendously changed the way the technique is used. Thus, a combinational approach using electrospinning, electrospraying and 3D printing has emerged.

Modern Practical Healthcare Issues in Biomedical Instrumentation describes the designs, applications and principles of several medical devices used in hospitals and at home. The book presents practical devices that can potentially be used for healthcare purposes. Sections cover the use of biosensors to monitor the physiological properties of the human body, focusing on devices used to evaluate, measure and manipulate the biological system, and highlighting practical devices that can potentially be used for healthcare purposes. It is an excellent resource for undergraduate, graduate and post-graduate students of biomedical engineering.

In its second, extensively revised second edition, Semiconducting Silicon Nanowires for Biomedical Applications reviews the fabrication, properties, and biomedical applications of this key material. The book begins by reviewing the basics of growth, characterization, biocompatibility, and surface modification of semiconducting silicon nanowires. Attention then turns to use of these structures for tissue engineering and delivery applications, followed by detection and sensing. Reflecting the evolution of this multidisciplinary subject, several new key topics are highlighted, including our understanding of the cell-nanowire interface, latest advances in associated morphologies (including silicon nanoneedles and nanotubes for therapeutic delivery), and significantly, the status of silicon nanowire commercialization in biotechnology. Semiconducting Silicon Nanowires for Biomedical Applications is a comprehensive resource for biomaterials scientists who are focused on biosensors, drug delivery, and the next generation of nano-biotech platforms that require a detailed understanding of the cell-nanowire interface, along with researchers and developers in industry and academia who are concerned with nanoscale biomaterials, in particular electronically-responsive structures.

Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images.

Brain Seizure Detection and Classification Using Electroencephalographic Signals presents EEG signal processing and analysis with high performance feature extraction. The book covers the feature selection method based on One-way ANOVA, along with high performance machine learning classifiers for the classification of EEG signals in normal and epileptic EEG signals. In addition, the authors also present new methods of feature extraction, including Singular Spectrum-Empirical Wavelet Transform (SSEWT) for improved classification of seizures in significant seizure-types, specifically epileptic and Non-Epileptic Seizures (NES). The performance of the system is compared with existing methods of feature extraction using Wavelet Transform (WT) and Empirical Wavelet Transform (EWT). The book's objective is to analyze the EEG signals to observe abnormalities of brain activities called epileptic seizure. Seizure is a neurological disorder in which too many neurons are excited at the same time and are triggered by brain injury or by chemical imbalance.

State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their applications across a wide range of clinical diagnoses. Advances in the role of machine learning, artificial intelligence, deep learning, cognitive image processing and suitable data analytics useful for clinical diagnosis and research applications are covered, including relevant case studies. The application of Neural Network, Artificial Intelligence, and Machine Learning methods in biomedical image analysis have resulted in the development of computer-aided diagnostic (CAD) systems that aim towards the automatic early detection of several severe diseases. State of the Art in Neural Networks and Their Applications is presented in two volumes. Volume 1 covers the state-of-the-art deep learning approaches for the detection of renal, retinal, breast, skin, and dental abnormalities and more.

Micro and Nano Systems for Biophysical Studies of Cells and Small Organisms provides a comprehensive introduction to the state-of-the-art micro and nano systems that have recently been developed and applied to biophysical studies of cells and small organisms. These micro and nano systems span from microelectromechanical systems (MEMS) and microfluidic devices to robotic micro-nanomanipulation systems. These biophysical studies range from cell mechanics to the neural science of worms and Drosophila. This book will help readers understand the fundamentals surrounding the development of these tools and teach them the most recent advances in cellular and organismal biophysics enabled by these technologies.

Neural Engineering for Autism Spectrum Disorder, Volume One: Imaging and Signal Analysis Techniques presents the latest advances in neural engineering and biomedical engineering as applied to the clinical diagnosis and treatment of Autism Spectrum Disorder (ASD). Advances in the role of neuroimaging, infrared spectroscopy, sMRI, fMRI, DTI, social behaviors and suitable data analytics useful for clinical diagnosis and research applications for Autism Spectrum Disorder are covered, including relevant case studies. The application of brain signal evaluation, EEG analytics, feature selection, and analysis of blood oxygen level-dependent (BOLD) signals are presented for detection and estimation of the degree of ASD.

Polysaccharide-Based Nanocomposites for Gene Delivery and Tissue Engineering presents quantitative background on new polysaccharide nanocomposites in a clear and logical way, highlighting the most exciting applications in gene delivery and tissue engineering and their progress. The book focuses on the different types of polysaccharide nanocomposites for gene delivery and tissue engineering and covers polysaccharide hydrogels for tissue engineering and polysaccharide magnetic nanocomposites for gene delivery. Chapters cover various nanocomposites presented in twenty-one separate chapters. This book will be of great interest to all those researching the development and applications of polysaccharide-based nanocomposites for modeling. As polysaccharide-based nanocomposites promise cutting-edge applications in gene delivery and tissue engineering, with their development at the forefront of modern medicine, this book is a welcome title on this exciting science.