Machine Learning in Dentistry

by Ching-Chang Ko

Author	: Ching-Chang Ko
Publisher	: Springer Nature
Total Pages	: 186
Release	: 2021-07-24
Genre	: Medical
ISBN	: 3030718816

GET BOOK

This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work.

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

by Wojciech Samek

Author	: Wojciech Samek
Publisher	: Springer Nature
Total Pages	: 435
Release	: 2019-09-10
Genre	: Computers
ISBN	: 3030289540

GET BOOK

The development of “intelligent” systems that can take decisions and perform autonomously might lead to faster and more consistent decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to “intelligent” machines. For sensitive tasks involving critical infrastructures and affecting human well-being or health, it is crucial to limit the possibility of improper, non-robust and unsafe decisions and actions. Before deploying an AI system, we see a strong need to validate its behavior, and thus establish guarantees that it will continue to perform as expected when deployed in a real-world environment. In pursuit of that objective, ways for humans to verify the agreement between the AI decision structure and their own ground-truth knowledge have been explored. Explainable AI (XAI) has developed as a subfield of AI, focused on exposing complex AI models to humans in a systematic and interpretable manner. The 22 chapters included in this book provide a timely snapshot of algorithms, theory, and applications of interpretable and explainable AI and AI techniques that have been proposed recently reflecting the current discourse in this field and providing directions of future development. The book is organized in six parts: towards AI transparency; methods for interpreting AI systems; explaining the decisions of AI systems; evaluating interpretability and explanations; applications of explainable AI; and software for explainable AI.

Machine Learning and Deep Learning Techniques for Medical Science

by K. Gayathri Devi

Author	: K. Gayathri Devi
Publisher	: CRC Press
Total Pages	: 413
Release	: 2022-05-11
Genre	: Technology & Engineering
ISBN	: 1000582523

GET BOOK

The application of machine learning is growing exponentially into every branch of business and science, including medical science. This book presents the integration of machine learning (ML) and deep learning (DL) algorithms that can be applied in the healthcare sector to reduce the time required by doctors, radiologists, and other medical professionals for analyzing, predicting, and diagnosing the conditions with accurate results. The book offers important key aspects in the development and implementation of ML and DL approaches toward developing prediction tools and models and improving medical diagnosis. The contributors explore the recent trends, innovations, challenges, and solutions, as well as case studies of the applications of ML and DL in intelligent system-based disease diagnosis. The chapters also highlight the basics and the need for applying mathematical aspects with reference to the development of new medical models. Authors also explore ML and DL in relation to artificial intelligence (AI) prediction tools, the discovery of drugs, neuroscience, diagnosis in multiple imaging modalities, and pattern recognition approaches to functional magnetic resonance imaging images. This book is for students and researchers of computer science and engineering, electronics and communication engineering, and information technology; for biomedical engineering researchers, academicians, and educators; and for students and professionals in other areas of the healthcare sector. Presents key aspects in the development and the implementation of ML and DL approaches toward developing prediction tools, models, and improving medical diagnosis Discusses the recent trends, innovations, challenges, solutions, and applications of intelligent system-based disease diagnosis Examines DL theories, models, and tools to enhance health information systems Explores ML and DL in relation to AI prediction tools, discovery of drugs, neuroscience, and diagnosis in multiple imaging modalities Dr. K. Gayathri Devi is a Professor at the Department of Electronics and Communication Engineering, Dr. N.G.P Institute of Technology, Tamil Nadu, India. Dr. Kishore Balasubramanian is an Assistant Professor (Senior Scale) at the Department of EEE at Dr. Mahalingam College of Engineering & Technology, Tamil Nadu, India. Dr. Le Anh Ngoc is a Director of Swinburne Innovation Space and Professor in Swinburne University of Technology (Vietnam).

Machine Learning

by Andrea Mechelli

Author	: Andrea Mechelli
Publisher	: Academic Press
Total Pages	: 412
Release	: 2019-11-14
Genre	: Medical
ISBN	: 0128157402

GET BOOK

Machine Learning is an area of artificial intelligence involving the development of algorithms to discover trends and patterns in existing data; this information can then be used to make predictions on new data. A growing number of researchers and clinicians are using machine learning methods to develop and validate tools for assisting the diagnosis and treatment of patients with brain disorders. Machine Learning: Methods and Applications to Brain Disorders provides an up-to-date overview of how these methods can be applied to brain disorders, including both psychiatric and neurological disease. This book is written for a non-technical audience, such as neuroscientists, psychologists, psychiatrists, neurologists and health care practitioners. - Provides a non-technical introduction to machine learning and applications to brain disorders - Includes a detailed description of the most commonly used machine learning algorithms as well as some novel and promising approaches - Covers the main methodological challenges in the application of machine learning to brain disorders - Provides a step-by-step tutorial for implementing a machine learning pipeline to neuroimaging data in Python

Artificial Intelligence and Machine Learning in 2D/3D Medical Image Processing

by Rohit Raja

Author	: Rohit Raja
Publisher	: CRC Press
Total Pages	: 181
Release	: 2020-12-23
Genre	: Technology & Engineering
ISBN	: 1000337138

GET BOOK

Digital images have several benefits, such as faster and inexpensive processing cost, easy storage and communication, immediate quality assessment, multiple copying while preserving quality, swift and economical reproduction, and adaptable manipulation. Digital medical images play a vital role in everyday life. Medical imaging is the process of producing visible images of inner structures of the body for scientific and medical study and treatment as well as a view of the function of interior tissues. This process pursues disorder identification and management. Medical imaging in 2D and 3D includes many techniques and operations such as image gaining, storage, presentation, and communication. The 2D and 3D images can be processed in multiple dimensions. Depending on the requirement of a specific problem, one must identify various features of 2D or 3D images while applying suitable algorithms. These image processing techniques began in the 1960s and were used in such fields as space, clinical purposes, the arts, and television image improvement. In the 1970s, with the development of computer systems, the cost of image processing was reduced and processes became faster. In the 2000s, image processing became quicker, inexpensive, and simpler. In the 2020s, image processing has become a more accurate, more efficient, and self-learning technology. This book highlights the framework of the robust and novel methods for medical image processing techniques in 2D and 3D. The chapters explore existing and emerging image challenges and opportunities in the medical field using various medical image processing techniques. The book discusses real-time applications for artificial intelligence and machine learning in medical image processing. The authors also discuss implementation strategies and future research directions for the design and application requirements of these systems. This book will benefit researchers in the medical image processing field as well as those looking to promote the mutual understanding of researchers within different disciplines that incorporate AI and machine learning. FEATURES Highlights the framework of robust and novel methods for medical image processing techniques Discusses implementation strategies and future research directions for the design and application requirements of medical imaging Examines real-time application needs Explores existing and emerging image challenges and opportunities in the medical field

Machine Learning Methods for Signal, Image and Speech Processing

by Meerja Akhil Jabbar

Author	: Meerja Akhil Jabbar
Publisher	:
Total Pages	: 250
Release	: 2021-11-30
Genre	:
ISBN	: 9788770223690

GET BOOK

The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and image analysis as well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests, etc. This book focuses on AI utilization in the speech, image, communications and virtual reality domains.

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics

by Pradeep N

Author	: Pradeep N
Publisher	: Academic Press
Total Pages	: 374
Release	: 2021-06-10
Genre	: Science
ISBN	: 0128220449

GET BOOK

Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians. - Provides a comprehensive reference for biomedical engineers, computer scientists, advanced industry practitioners, researchers, and clinicians to understand and develop healthcare analytics using advanced tools and technologies - Includes in-depth illustrations of advanced techniques via dataset samples, statistical tables, and graphs with algorithms and computational methods for developing new applications in healthcare informatics - Unique case study approach provides readers with insights for practical clinical implementation

Dentistry

by Kirti Chawla

Author	: Kirti Chawla
Publisher	: Nova Science Publishers
Total Pages	: 330
Release	: 2021
Genre	: Medical
ISBN	: 9781536190885

GET BOOK

"This book focuses on the future direction of dental research in every discipline. In this context, dental research must be deliverable to society, not just to churn out scientific publications but to truly change protocols applied in the clinic. Most of the topics covered in this book discuss the current trends and future advances in clinical dentistry as well as dental research. The book covers advances in research, future perspectives and challenges faced in different disciplines of dentistry. Various chapters discuss the recent advances in materials, methods and diagnostics, as well as their future implications. The contents review the applications of big data, artificial intelligence and machine learning in various branches of dentistry like orthodontics, prosthodontics, periodontics, pediatric dentistry, endodontics and oral and maxillofacial surgery. With the world moving into the digital age and advancing technology, we have collated the latest developments in the field of dental research"--

Color and Appearance in Dentistry

by Alvaro Della Bona

Author	: Alvaro Della Bona
Publisher	: Springer Nature
Total Pages	: 149
Release	: 2020-04-03
Genre	: Medical
ISBN	: 3030426262

GET BOOK

This book presents the state of the art in color science and explains its application to dental structures and materials, using high-quality illustrations to ensure ease of learning. Most people seek a bright smile with a natural appearance. This goal often poses a great clinical challenge for the dentist, and its achievement is dependent on a good knowledge of color science and optical properties relevant to dentistry. Further, if a smile is to be esthetically improved to the patient’s satisfaction, the dentist must be able to extract the best from dental materials and techniques, must understand all aspects of facial harmony, and must communicate effectively with both the patient and lab technicians. All of these aspects are thoroughly explored in the book, with detailed coverage of such topics as visual and instrumental shade matching, color management, and avoidance of complications and pitfalls. Color and Appearance in Dentistry will be of high value to all who are engaged in the daily practice of esthetic dentistry.