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See below for a selection of the latest books from Biomedical engineering category. Presented with a red border are the Biomedical engineering books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Biomedical engineering books and those from many more genres to read that will keep you inspired and entertained. And it's all free!
Machine Learning in Medicine covers the state-of-the-art techniques of machine learning and their applications in the medical field. It presents several Computer-aided diagnosis (CAD) systems, which have played an important role in the diagnosis of several diseases in the past decade e.g., cancer detection, resulting in the development of several successful systems. New developments in machine learning may make it possible in the near future to develop machines that are capable of completely performing tasks that currently cannot be completed without human aid, especially in the medical field. The book covers such machines, including: CNNs with different activation functions for small to medium size biomedical datasets; detection of abnormal activities stemming from cognitive decline, thermal dose modelling for thermal ablative cancer treatments; dermatological machine learning clinical decision support systems; artificial intelligence powered ultrasound for diagnosis; practical challenges with possible solutions for machine learning in medical imaging; epilepsy diagnosis from structural MRI; Alzheimer's disease diagnosis; classification of left ventricular hypertrophy; and intelligent medical language understanding. This book will help to advance scientific research within the broad field of machine learning in the medical field. The book focuses on major trends and challenges in this area, and it presents work aimed at identifying new techniques and their use in biomedical analysis, including extensive references at the end of each chapter.
Somatosensory Feedback for Neuroprosthetics covers all relevant aspects to facilitate learning, research and development in the field. The book starts with chapters reviewing the basic anatomy, physiology and psychophysics of the somatosensory system, sensorimotor control and instrumentation. Some sections are dedicated to invasive (peripheral and central, mainly cortical) and noninvasive (vibrotactile, electrotactile, etc.) approaches. Final chapters cover future technologies such as novel sensors and electrodes, safety, clinical testing, commercialization, and practical use, helping make up future prospects for this field with an emphasis on development and end-use. With contributions from renowned experts, users will find recent findings and technical details surrounding findings. Although somatosensory system works in tandem with the motor system in biology, the majority of the prosthetics research and commercial efforts had focused on accommodating movement deficits. With the development of neuroprostheses in the last 15 years, it has become evident that somatosensory input (mainly as touch and proprioception) is essential for motor control, manipulating objects, and embodiment, in addition to its primary role for sensory perception.
Biomaterials have existed for millennia as mechanical replacement structures following disease or injury. Biomaterial design has changed markedly from structural support with an inert immune profile as the primary objective to designs that elicit an integrative local tissue response and a pro-repair immune cell phenotype. Immunomodulatory Biomaterials: Regulating the Immune Response with Biomaterials to Affect Clinical Outcome offers a single, comprehensive reference on biomaterials for modulation of the host response, for materials scientists, tissue engineers and those working in regenerative medicine. This book details methods, materials and strategies designed to regulate the host immune response following surgical implantation and thus facilitate specific local cell infiltration and tissue deposition. There has been a dramatic transformation in our understanding of the role of the immune system, both innate and adaptive; these changes include recognition of the plasticity of immune cells, especially macrophages, cross-talk between the immune system and stem cells, and the necessity for in situ transition between inflammatory and regulatory immune cell phenotypes. The exploitation of these findings and the design and manufacture of new biomaterials is occurring at an astounding pace. There is currently no book directed at the interdisciplinary principles guiding the design, manufacture, testing, and clinical translation of biomaterials that proactively regulate the host tissue immune response. The challenge for academia, industry, and regulatory agencies to encourage innovation while assuring safety and maximizing efficacy has never been greater. Given the highly interdisciplinary requirements for the design, manufacture and use of immunomodulatory biomaterials, this book will prove a useful single resource across disciplines.
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).
This new volume discusses state-of-the-art deep learning techniques and approaches that can be applied in biomedical systems and health informatics. Deep learning in the biomedical field is an effective method of collecting and analyzing data that can be used for the accurate diagnosis of disease. This volume delves into a variety of applications, techniques, algorithms, platforms, and tools used in this area, such as image segmentation, classification, registration, and computer-aided analysis. The editors proceed on the principle that accurate diagnosis of disease depends on image acquisition and interpretation. There are many methods to get high resolution radiological images, but we are still lacking in automated image interpretation. Currently deep learning techniques are providing a feasible solution for automatic diagnosis of disease with good accuracy. Analyzing clinical data using deep learning techniques enables clinicians to diagnose diseases at an early stage and treat the patients more effectively. Chapters explore such approaches as deep learning algorithms, convolutional neural networks and recurrent neural network architecture, image stitching techniques, deep RNN architectures, and more. The volume also depicts how deep learning techniques can be applied for medical diagnostics of several specific health scenarios, such as cancer, COVID-19, acute neurocutaneous syndrome, cardiovascular and neuro diseases, skin lesions and skin cancer, etc. Key features: Introduces important recent technological advancements in the field Describes the various techniques, platforms, and tools used in biomedical deep learning systems Includes informative case studies that help to explain the new technologies Handbook of Deep Learning in Biomedical Engineering and Health Informatics provides a thorough exploration of biomedical systems applied with deep learning techniques and will provide valuable information for researchers, medical and industry practitioners, academicians, and students.
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.
Discover the fundamental principles of biomedical measurement design and performance evaluation with this hands-on guide. Whether you develop measurement instruments or use them in novel ways, this practical text will prepare you to be an effective generator and consumer of biomedical data. Designed for both classroom instruction and self-study, it explains how information is encoded into recorded data and can be extracted and displayed in an accessible manner. Describes and integrates experimental design, performance assessment, classification, and system modelling. Combines mathematical concepts with computational models, providing the tools needed to answer advanced biomedical questions. Includes MATLAB (R) scripts throughout to help readers model all types of biomedical systems, and contains numerous homework problems, with a solutions manual available online. This is an essential text for advanced undergraduate and graduate students in bioengineering, electrical and computer engineering, computer science, medical physics, and anyone preparing for a career in biomedical sciences and engineering.
This collection of research articles and reviews covers the latest work in the design, delivery, dynamic abilities, and immune stimulation of RNA nanoparticles which have driven the utilization of their immunomodulatory properties. The unknown immune properties of nucleic acid nanoparticles have been a major hurdle in their adaptation until the works herein began assessing their structure-activity relationships. This collection chronologically follows the path of investigating the recognition of design components to implementing them into nucleic acid nanostructures. RNA nanotechnology is an emerging platform for therapeutics with increasing clinical relevance as this approach becomes more widely used and approved for the treatment of various diseases. The latest research aims to take advantage of RNA's modular nature for the design of nanostructures which can interact with their environments to communicate programmed messages with intracellular pathways. In doing so, nanoparticles can be used to elicit or elude responses by the immune system as desired in conjunction with their therapeutic applications.
This book examines the use of biomedical signal processing-EEG, EMG, and ECG-in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases. The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications. FEATURES Examines modeling and acquisition of biomedical signals of different disorders Discusses CAD-based analysis of diagnosis useful for healthcare Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes case studies and research directions, including novel approaches used in advanced healthcare systems This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.
This book presents the most recent research and applications in Biomedical Engineering, electronic health and TeleMedicine. Top-scholars and research leaders in the field contributed to the book. It covers a broad range of applications including smart platforms like DietHub which connects patients with doctors online. The book highlights the advantages of Telemedicine to improve the healthcare services and how it can contribute to the homogenization of medicine without any geographical barriers. Telemedicine transforms local hospitals, with limited services, into a node of an integrated network. In this manner, these nodes start to play an important role in preventive medicine and in high-level management of chronic diseases. The authors also discuss the challenges related to health informatics and in e-health management . The topics of the book include: synchronous and asynchronous telemedicine with deep discussions on e-health applications, virtual medical assistance, real-time virtual visits, digital telepathology, home health monitoring, and medication adherence, wearable sensors, tele-monitoring hubs and sensors, Internet of Things, augmented and virtual reality as well as e-learning technologies. The scope of the book is quite unique particularly in terms of the application domains that it targets. It is a unique hub for the dissemination of state of the art research in the telemedicine field and healthcare ecosystems. The book is a reference for graduate students, doctors, and researchers to discover the most recent findings, and hence, it achieves breakthroughs and pushes the boundaries in the related fields.
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.