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| artificial intelligence in radiation therapy: Machine Learning in Radiation Oncology Issam El Naqa, Ruijiang Li, Martin J. Murphy, 2015-06-19 This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities. |
| artificial intelligence in radiation therapy: Artificial Intelligence in Medical Imaging Erik R. Ranschaert, Sergey Morozov, Paul R. Algra, 2019-01-29 This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals. |
| artificial intelligence in radiation therapy: Artificial Intelligence in Radiation Oncology and Biomedical Physics Gilmer Valdes, Lei Xing, 2023-08-14 This pioneering book explores how machine learning and other AI techniques impact millions of cancer patients who benefit from ionizing radiation. It features contributions from global researchers and clinicians, focusing on the clinical applications of machine learning for medical physics. AI and machine learning have attracted much recent attention and are being increasingly adopted in medicine, with many clinical components and commercial software including aspects of machine learning integration. General principles and important techniques in machine learning are introduced, followed by discussion of clinical applications, particularly in radiomics, outcome prediction, registration and segmentation, treatment planning, quality assurance, image processing, and clinical decision-making. Finally, a futuristic look at the role of AI in radiation oncology is provided. This book brings medical physicists and radiation oncologists up to date with the most novel applications of machine learning to medical physics. Practitioners will appreciate the insightful discussions and detailed descriptions in each chapter. Its emphasis on clinical applications reaches a wide audience within the medical physics profession. |
| artificial intelligence in radiation therapy: Artificial Intelligence In Radiation Oncology Seong K Mun, Sonja Dieterich, 2022-12-27 The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology. |
| artificial intelligence in radiation therapy: Adaptive Radiation Therapy X. Allen Li, 2011-01-27 Modern medical imaging and radiation therapy technologies are so complex and computer driven that it is difficult for physicians and technologists to know exactly what is happening at the point-of-care. Medical physicists responsible for filling this gap in knowledge must stay abreast of the latest advances at the intersection of medical imaging an |
| artificial intelligence in radiation therapy: Big Data in Radiation Oncology Jun Deng, Lei Xing, 2019-03-07 Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship. |
| artificial intelligence in radiation therapy: Machine Learning and Artificial Intelligence in Radiation Oncology Barry S. Rosenstein, Tim Rattay, John Kang, 2023-12-02 Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology. - Presents content written by practicing clinicians and research scientists, allowing a healthy mix of both new clinical ideas as well as perspectives on how to translate research findings into the clinic - Provides perspectives from artificial intelligence (AI) industry researchers to discuss novel theoretical approaches and possibilities on academic collaborations - Brings diverse points-of-view from an international group of experts to provide more balanced viewpoints on a complex topic |
| artificial intelligence in radiation therapy: The Modern Technology of Radiation Oncology Jake Van Dyk, 1999 Details technology associated with radiation oncology, emphasizing design of all equipment allied with radiation treatment. Describes procedures required to implement equipment in clinical service, covering needs assessment, purchase, acceptance, and commissioning, and explains quality assurance issues. Also addresses less common and evolving technologies. For medical physicists and radiation oncologists, as well as radiation therapists, dosimetrists, and engineering technologists. Includes bandw medical images and photos of equipment. Paper edition (unseen), $145.95. Annotation copyrighted by Book News, Inc., Portland, OR |
| artificial intelligence in radiation therapy: Artificial Intelligence in Radiation Therapy Iori Sumida, 2022-10-31 Artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. The book provides applications of artificial intelligence (AI) in radiation therapy according to the clinical radiotherapy workflow. An introductory section explains the necessity of AI regarding accuracy and efficiency in clinical settings followed by a basic learning method and introduction of potential applications in radiotherapy. Some chapters also include typical source codes which the reader may use in their original neural network. This book would be an excellent text for more experienced practitioners and researchers and members of medical physics communities, such as AAPM, ASTRO, and ESTRO. Students and graduate students who are focusing on medical physics would also benefit from this text. Key Features: Systematic chapters are designed over the radiotherapy workflow. Describes in detail how AI contributes to a perspective in the future radiotherapy. Typical source codes are included to implement a neural network. The book supports the reader as a developer of AI. |
| artificial intelligence in radiation therapy: Radiomics and Radiogenomics Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, 2019-07-09 Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation |
| artificial intelligence in radiation therapy: Artificial Intelligence in Radiation Therapy Dan Nguyen, Lei Xing, Steve Jiang, 2019-10-10 This book constitutes the refereed proceedings of the First International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019, held in conjunction with MICCAI 2019 in Shenzhen, China, in October 2019. The 20 full papers presented were carefully reviewed and selected from 24 submissions. The papers discuss the state of radiation therapy, the state of AI and related technologies, and hope to find a pathway to revolutionizing the field to ultimately improve cancer patient outcome and quality of life. |
| artificial intelligence in radiation therapy: Machine Learning and Medical Imaging Guorong Wu, Dinggang Shen, Mert Sabuncu, 2016-08-11 Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, and clinicians. - Demonstrates the application of cutting-edge machine learning techniques to medical imaging problems - Covers an array of medical imaging applications including computer assisted diagnosis, image guided radiation therapy, landmark detection, imaging genomics, and brain connectomics - Features self-contained chapters with a thorough literature review - Assesses the development of future machine learning techniques and the further application of existing techniques |
| artificial intelligence in radiation therapy: Emerging Models for Global Health Wilfred Ngwa, Twalib Ngoma, 2016 In response to the growing global health challenge in the fight against cancer, there is now a greater need for radiation oncology health professionals across institutions to collaborate and be more globally engaged. Emerging Models for Global Health in Radiation Oncology is a response to the need for a book that comprehensively covers the important and emerging field of radiation oncology. The recent World Health Organization (WHO) Cancer Report describes the growing global burden of cancer as alarming, a major obstacle to human development and well-being, with a growing annual economic cost of ca. US$1.16 trillion. The report also highlights major global cancer disparities, and these major disparities in cancer deaths are in part a reflection of poignant underlying differences in radiation oncology services. In parallel with this, there has been a major recent upsurge in radiation oncology global health interest and a common issue expressed at global health summits, seminars, and symposia is that people want to participate in global health but do not know how. This insightful book highlights the emerging models for global radiation oncology, and serves as a useful resource to facilitate participation and greater effective collaborative global cancer care, research, education, and outreach. It is suitable for researchers, students, health professionals, and anyone interested in the global oncology community. |
| artificial intelligence in radiation therapy: Gynecologic Radiation Oncology: A Practical Guide Patricia Eifel, Ann H. Klopp, 2016-07-13 Offering practical approaches to common clinical problems, Gynecologic Radiation Oncology: A Practical Guide compiles the extensive clinical experience of Drs. Patricia J. Eifel and Ann H. Klopp from MD Anderson Cancer Center into one user-friendly volume. This reference addresses practical aspects of the field: how to evaluate the role of radiation therapy in various clinical settings, how to explain the rationale for treatment recommendations to referring physicians and patients, when and how to apply various external beam and brachytherapy techniques to address specific clinical problems, and how to monitor and manage patients during and after treatment. The book focuses on the following items, which can have immediate application to the treatment of patients with gynecologic cancers. |
| artificial intelligence in radiation therapy: Artificial Intelligence , 2019-07-31 Artificial intelligence (AI) is taking on an increasingly important role in our society today. In the early days, machines fulfilled only manual activities. Nowadays, these machines extend their capabilities to cognitive tasks as well. And now AI is poised to make a huge contribution to medical and biological applications. From medical equipment to diagnosing and predicting disease to image and video processing, among others, AI has proven to be an area with great potential. The ability of AI to make informed decisions, learn and perceive the environment, and predict certain behavior, among its many other skills, makes this application of paramount importance in today's world. This book discusses and examines AI applications in medicine and biology as well as challenges and opportunities in this fascinating area. |
| artificial intelligence in radiation therapy: Oxford Handbook of Ethics of AI Markus D. Dubber, Frank Pasquale, Sunit Das, 2020-06-30 This volume tackles a quickly-evolving field of inquiry, mapping the existing discourse as part of a general attempt to place current developments in historical context; at the same time, breaking new ground in taking on novel subjects and pursuing fresh approaches. The term A.I. is used to refer to a broad range of phenomena, from machine learning and data mining to artificial general intelligence. The recent advent of more sophisticated AI systems, which function with partial or full autonomy and are capable of tasks which require learning and 'intelligence', presents difficult ethical questions, and has drawn concerns from many quarters about individual and societal welfare, democratic decision-making, moral agency, and the prevention of harm. This work ranges from explorations of normative constraints on specific applications of machine learning algorithms today-in everyday medical practice, for instance-to reflections on the (potential) status of AI as a form of consciousness with attendant rights and duties and, more generally still, on the conceptual terms and frameworks necessarily to understand tasks requiring intelligence, whether human or A.I. |
| artificial intelligence in radiation therapy: Khan's The Physics of Radiation Therapy Faiz M. Khan, John P. Gibbons, 2014-04-03 Expand your understanding of the physics and practical clinical applications of advanced radiation therapy technologies with Khan's The Physics of Radiation Therapy, 5th edition, the book that set the standard in the field. This classic full-color text helps the entire radiation therapy team—radiation oncologists, medical physicists, dosimetrists, and radiation therapists—develop a thorough understanding of 3D conformal radiotherapy (3D-CRT), stereotactic radiosurgery (SRS), high dose-rate remote afterloaders (HDR), intensity modulated radiation therapy (IMRT), image-guided radiation therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), and proton beam therapy, as well as the physical concepts underlying treatment planning, treatment delivery, and dosimetry. In preparing this new Fifth Edition, Dr. Kahn and new co-author Dr. John Gibbons made chapter-by-chapter revisions in the light of the latest developments in the field, adding new discussions, a new chapter, and new color illustrations throughout. Now even more precise and relevant, this edition is ideal as a reference book for practitioners, a textbook for students, and a constant companion for those preparing for their board exams. Features Stay on top of the latest advances in the field with new sections and/or discussions of Image Guided Radiation Therapy (IGRT), Volumetric Modulated Arc Therapy (VMAT), and the Failure Mode Event Analysis (FMEA) approach to quality assurance. Deepen your knowledge of Stereotactic Body Radiotherapy (SBRT) through a completely new chapter that covers SBRT in greater detail. Expand your visual understanding with new full color illustrations that reflect current practice and depict new procedures. Access the authoritative information you need fast through the new companion website which features fully searchable text and an image bank for greater convenience in studying and teaching. This is the tablet version which does not include access to the supplemental content mentioned in the text. |
| artificial intelligence in radiation therapy: Monte Carlo Techniques in Radiation Therapy Frank Verhaegen, Joao Seco, 2021-11-29 About ten years after the first edition comes this second edition of Monte Carlo Techniques in Radiation Therapy: Introduction, Source Modelling, and Patient Dose Calculations, thoroughly updated and extended with the latest topics, edited by Frank Verhaegen and Joao Seco. This book aims to provide a brief introduction to the history and basics of Monte Carlo simulation, but again has a strong focus on applications in radiotherapy. Since the first edition, Monte Carlo simulation has found many new applications, which are included in detail. The applications sections in this book cover the following: Modelling transport of photons, electrons, protons, and ions Modelling radiation sources for external beam radiotherapy Modelling radiation sources for brachytherapy Design of radiation sources Modelling dynamic beam delivery Patient dose calculations in external beam radiotherapy Patient dose calculations in brachytherapy Use of artificial intelligence in Monte Carlo simulations This book is intended for both students and professionals, both novice and experienced, in medical radiotherapy physics. It combines overviews of development, methods, and references to facilitate Monte Carlo studies. |
| artificial intelligence in radiation therapy: Practical Radiation Oncology Supriya Mallick, Goura K. Rath, Rony Benson, 2019-11-25 This book addresses the most relevant aspects of radiation oncology in terms of technical integrity, dose parameters, machine and software specifications, as well as regulatory requirements. Radiation oncology is a unique field that combines physics and biology. As a result, it has not only a clinical aspect, but also a physics aspect and biology aspect, all three of which are inter-related and critical to optimal radiation treatment planning. In addition, radiation oncology involves a host of machines/software. One needs to have a firm command of these machines and their specifications to deliver comprehensive treatment. However, this information is not readily available, which poses serious challenges for students learning the planning aspect of radiation therapy. In response, this book compiles these relevant aspects in a single source. Radiation oncology is a dynamic field, and is continuously evolving. However, tracking down the latest findings is both difficult and time-consuming. Consequently, the book also comprehensively covers the most important trials. Offering an essential ready reference work, it represents a value asset for all radiation oncology practitioners, trainees and students. |
| artificial intelligence in radiation therapy: Quality Assurance in Radiotherapy , 1988-01-01 |
| artificial intelligence in radiation therapy: Introduction to Algorithms for Data Mining and Machine Learning Xin-She Yang, 2019-06-17 Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages |
| artificial intelligence in radiation therapy: Image-Guided IMRT Thomas Bortfeld, Rupert Schmidt-Ullrich, Wilfried De Neve, David E. Wazer, 2006-05-28 Intensity-modulated radiation therapy (IMRT), one of the most important developments in radiation oncology in the past 25 years, involves technology to deliver radiation to tumors in the right location, quantity and time. Unavoidable irradiation of surrounding normal tissues is distributed so as to preserve their function. The achievements and future directions in the field are grouped in the three sections of the book, each suitable for supporting a teaching course. Part 1 contains topical reviews of the basic principles of IMRT, part 2 describes advanced techniques such as image-guided and biologically based approaches, and part 3 focuses on investigation of IMRT to improve outcome at various cancer sites. |
| artificial intelligence in radiation therapy: Molecular Targeted Radiosensitizers Henning Willers, Iris Eke, 2020-08-10 Molecular Targeted Radiosensitizers: Opportunities and Challenges provides the reader with a comprehensive review of key pre-clinical research components required to identify effective radiosensitizing drugs. The book features discussions on the mechanisms and markers of clinical radioresistance, pre-clinical screening of targeted radiosensitizers, 3D radiation biology for studying radiosensitizers, in vivo determinations of local tumor control, genetically engineered mouse models for studying radiosensitizers, targeting the DNA damage response for radiosensitization, targeting tumor metabolism to overcome radioresistance, radiosensitizers in the era of immuno-oncology, and more. Additionally, the book features discussions on high-throughput drug screening, predictive biomarkers, pre-clinical tumor models, and the influence of the tumor microenvironment and the immune system, with a specific focus on the challenges radiation oncologists and medical oncologists currently face in testing radiosensitizers in human cancers. Edited by two acclaimed experts in radiation biology and radiosensitizers, with thirteen chapters contributed by experts, this new volume presents an in-depth look at current developments within a rapidly moving field, with a look at where the field will be heading and providing comprehensive insight into the framework of targeted radiosensitzer development. Essential reading for investigators in cancer research and radiation biology. |
| artificial intelligence in radiation therapy: Tele-oncology Giovanna Gatti, Gabriella Pravettoni, Fabio Capello, 2015-06-09 This book explains how telemedicine can offer solutions capable of improving the care and survival rates of cancer patients and can also help patients to live a normal life in spite of their condition. Different fields of application – community, hospital and home based – are examined, and detailed attention is paid to the use of tele-oncology in rural/extreme rural settings and in developing countries. The impact of new technologies and the opportunities afforded by the social web are both discussed. The concluding chapters consider eLearning in relation to cancer care and assess the scope for education to improve prevention. No medical condition can shatter people’s lives as cancer does today and the need to develop strategies to reduce the disease burden and improve quality of life is paramount. Readers will find this new volume in Springer’s TELe Health series to be a rich source of information on the important contribution that can be made by telemedicine in achieving these goals. |
| artificial intelligence in radiation therapy: Developing Cognitive Bots Using the IBM Watson Engine Navin Sabharwal, Sudipta Barua, Neha Anand, Pallavi Aggarwal, 2019-12-14 Cognitive Virtual Bots are taking the technology and user experience world by storm. This book provides clear guidance on how different cognitive platforms can be used to develop Cognitive Virtual Assistants that enable a conversation by using DialogFlow and advanced Natural Language Processing. You will start by understanding the technology landscape and various use cases that Cognitive Virtual Assistants can be used in. Early chapters will take you through the basics of Cognitive Virtual Assistants, before moving onto advanced concepts and hands on examples of using IBM Watson Assistant and its advanced configurations with Watson Discovery Services, Watson Knowledge Studio and Spellchecker Service. You'll then examine integrations that enrich the Cognitive Virtual Assistant by providing data around weather, locations, stock markets. The book concludes by providing a glimpse of what to expect in the future for Cognitive Virtual Assistants. What You'll Learn Review the fundamentals of Cognitive Virtual Assistants.Develop a Cognitive Virtual Assistant from scratch using IBM Watson platform.Integrate and enrich your Virtual Agent with other services such as weather, location and stocks.Instantly deliver your bot on major messaging channels such as Skype, SMS, and WebchatTrain your Cognitive Virtual Agent on specific use cases.Who This Book Is ForAI and machine learning engineers, cognitive solutions architects and developers would find the book extremely useful |
| artificial intelligence in radiation therapy: Artificial Intelligence for Healthcare Applications and Management Boris Galitsky, Saveli Goldberg, 2022-01-19 Current conditions affected by COVID-19 pose new challenges for healthcare management and learning how to apply AI will be important for a broad spectrum of students and mature professionals working in medical informatics. . |
| artificial intelligence in radiation therapy: Breast Cancer Screening and Diagnosis Mahesh K Shetty, 2014-09-19 This book presents the current trends and practices in breast imaging. Topics include mammographic interpretation; breast ultrasound; breast MRI; management of the symptomatic breast in young, pregnant and lactating women; breast intervention with imaging pathological correlation; the postoperative breast and current and emerging technologies in breast imaging. It emphasizes the importance of fostering a multidisciplinary approach in the diagnosis and treatment of breast diseases. Featuring more than 800 high-resolution images and showcasing contributions from leading authorities in the screening, diagnosis and management of the breast cancer patient, Breast Cancer Screening and Diagnosis is a valuable resource for radiologists, oncologists and surgeons. |
| artificial intelligence in radiation therapy: Brownlie's Principles of Public International Law James Crawford, Ian Brownlie, 2019 Serving as a single volume introduction to the field as a whole, this ninth edition of Brownlie's Principles of International Law seeks to present international law as a system that is based on, and helps structure, relations among states and other entities at the international level. |
| artificial intelligence in radiation therapy: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis Danail Stoyanov, Zeike Taylor, Duygu Sarikaya, Jonathan McLeod, Miguel Angel González Ballester, Noel C.F. Codella, Anne Martel, Lena Maier-Hein, Anand Malpani, Marco A. Zenati, Sandrine De Ribaupierre, Luo Xiongbiao, Toby Collins, Tobias Reichl, Klaus Drechsler, Marius Erdt, Marius George Linguraru, Cristina Oyarzun Laura, Raj Shekhar, Stefan Wesarg, M. Emre Celebi, Kristin Dana, Allan Halpern, 2018-10-01 This book constitutes the refereed joint proceedings of the First International Workshop on OR 2.0 Context-Aware Operating Theaters, OR 2.0 2018, 5th International Workshop on Computer Assisted Robotic Endoscopy, CARE 2018, 7th International Workshop on Clinical Image-Based Procedures, CLIP 2018, and the First International Workshop on Skin Image Analysis, ISIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 11 full papers presented at OR 2.0 2018, the 5 full papers presented at CARE 2018, the 8 full papers presented at CLIP 2018, and the 10 full papers presented at ISIC 2018 were carefully reviewed and selected. The OR 2.0 papers cover a wide range of topics such as machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors. The CARE papers cover topics to advance the field of computer-assisted and robotic endoscopy. The CLIP papers cover topics to fill gaps between basic science and clinical applications. The ISIC papers cover topics to facilitate knowledge dissemination in the field of skin image analysis, as well as to host a melanoma detection challenge, raising awareness and interest for these socially valuable tasks. |
| artificial intelligence in radiation therapy: The British Chess Magazine; Volume 16 Anonymous, 2022-10-27 This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. |
| artificial intelligence in radiation therapy: Contouring in Head and Neck Cancer Peter C. Levendag, 2009 |
| artificial intelligence in radiation therapy: Auto-Segmentation for Radiation Oncology Jinzhong Yang, Gregory C. Sharp, Mark J. Gooding, 2021-04-18 This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine |
| artificial intelligence in radiation therapy: Advances in Radiation Therapy M. Guckenberger, S.E. Combs, D. Zips, 2018-04-12 Developments in radiation oncology have been key to the tremendous progress made in the field in recent years. The combination of optimal systemic treatment and local therapy has resulted in continuing improved outcomes of cancer therapy. This progress forms the basis for current pre-clinical and clinical research which will strengthen the position of radiation oncology as an essential component of oncological care. This book summarizes recent advances in radiotherapy research and clinical patient care. Topics include radiobiology, radiotherapy technology, and particle therapy. Chapters cover a summary and analysis of recent developments in the search for biomarkers for precision radiotherapy, novel imaging possibilities and treatment planning, and advances in understanding the differences between photon and particle radiotherapy. Advances in Radiation Therapy is an invaluable source of information for scientists and clinicians working in the field of radiation oncology. It is also a relevant resource for those interested in the broad topic of radiotherapy in general. |
| artificial intelligence in radiation therapy: Understanding and Interpreting Machine Learning in Medical Image Computing Applications Danail Stoyanov, Zeike Taylor, Seyed Mostafa Kia, Ipek Oguz, Mauricio Reyes, Anne Martel, Lena Maier-Hein, Andre F. Marquand, Edouard Duchesnay, Tommy Löfstedt, Bennett Landman, M. Jorge Cardoso, Carlos A. Silva, Sergio Pereira, Raphael Meier, 2018-10-23 This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis. |
| artificial intelligence in radiation therapy: Spectral Computed Tomography Björn J. Heismann, Bernhard T. Schmidt, Thomas Flohr, 2012 Computed tomography (CT) is a widely used x-ray scanning technique. In its prominent use as a medical imaging device, CT serves as a workhorse in many clinical settings throughout the world. It provides answers to urgent diagnostic tasks such as oncology tumor staging, acute stroke analysis, or radiation therapy planning. Spectral Computed Tomography provides a concise, practical coverage of this important medical tool. The first chapter considers the main clinical motivations for spectral CT applications. In Chapter 2, the measurement properties of spectral CT systems are described. Chapter 3 provides an overview of the current state of research on spectral CT algorithms. Based on this overview, the technical realization of spectral CT systems is evaluated in Chapter 4. Device approaches such as DSCT, kV switching, and energy-resolving detectors are compared. Finally, Chapter 5 summarizes various algorithms for spectral CT reconstructions and spectral CT image postprocessing, and links these algorithms to clinical use cases |
| artificial intelligence in radiation therapy: Surface Guided Radiation Therapy Jeremy David Page Hoisak, Adam Brent Paxton, Benjamin James Waghorn, Todd Pawlicki, 2020-02-13 Surface Guided Radiation Therapy provides a comprehensive overview of optical surface image guidance systems for radiation therapy. It serves as an introductory teaching resource for students and trainees, and a valuable reference for medical physicists, physicians, radiation therapists, and administrators who wish to incorporate surface guided radiation therapy (SGRT) into their clinical practice. This is the first book dedicated to the principles and practice of SGRT, featuring: Chapters authored by an internationally represented list of physicists, radiation oncologists and therapists, edited by pioneers and experts in SGRT Covering the evolution of localization systems and their role in quality and safety, current SGRT systems, practical guides to commissioning and quality assurance, clinical applications by anatomic site, and emerging topics including skin mark-less setups. Several dedicated chapters on SGRT for intracranial radiosurgery and breast, covering technical aspects, risk assessment and outcomes. Jeremy Hoisak, PhD, DABR is an Assistant Professor in the Department of Radiation Medicine and Applied Sciences at the University of California, San Diego. Dr. Hoisak’s clinical expertise includes radiosurgery and respiratory motion management. Adam Paxton, PhD, DABR is an Assistant Professor in the Department of Radiation Oncology at the University of Utah. Dr. Paxton’s clinical expertise includes patient safety, motion management, radiosurgery, and proton therapy. Benjamin Waghorn, PhD, DABR is the Director of Clinical Physics at Vision RT. Dr. Waghorn’s research interests include intensity modulated radiation therapy, motion management, and surface image guidance systems. Todd Pawlicki, PhD, DABR, FAAPM, FASTRO, is Professor and Vice-Chair for Medical Physics in the Department of Radiation Medicine and Applied Sciences at the University of California, San Diego. Dr. Pawlicki has published extensively on quality and safety in radiation therapy. He has served on the Board of Directors for the American Society for Radiology Oncology (ASTRO) and the American Association of Physicists in Medicine (AAPM). |
| artificial intelligence in radiation therapy: Medical Image Registration Joseph V. Hajnal, Derek L.G. Hill, 2001-06-27 Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid |
| artificial intelligence in radiation therapy: Statistical Regression and Classification Norman Matloff, 2017-09-19 Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. The text takes a modern look at regression: * A thorough treatment of classical linear and generalized linear models, supplemented with introductory material on machine learning methods. * Since classification is the focus of many contemporary applications, the book covers this topic in detail, especially the multiclass case. * In view of the voluminous nature of many modern datasets, there is a chapter on Big Data. * Has special Mathematical and Computational Complements sections at ends of chapters, and exercises are partitioned into Data, Math and Complements problems. * Instructors can tailor coverage for specific audiences such as majors in Statistics, Computer Science, or Economics. * More than 75 examples using real data. The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis. Norman Matloff is a professor of computer science at the University of California, Davis, and was a founder of the Statistics Department at that institution. His current research focus is on recommender systems, and applications of regression methods to small area estimation and bias reduction in observational studies. He is on the editorial boards of the Journal of Statistical Computation and the R Journal. An award-winning teacher, he is the author of The Art of R Programming and Parallel Computation in Data Science: With Examples in R, C++ and CUDA. |
| artificial intelligence in radiation therapy: Radiomics and Radiogenomics in Neuro-oncology Hassan Mohy-ud-Din, Saima Rathore, 2020-02-24 This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging. |
| artificial intelligence in radiation therapy: Nuclear Medicine in Oncology Gang Huang, 2019-06-11 This book introduces molecular imaging and Target Therapy in various cancers. The first part is the subjects and primary focused on the basics of nuclear physics, radiation dosimetry, nuclear medicine equipment and small animal imaging equipment. The second part is about the radiopharmaceutical and commonly used clinical radiopharmaceuticals, including positron emission imaging agent, single photon emission imaging agent, and radionuclide therapy agents as well as their radioactive preparation, quality control, and a brief clinical application were included. Also, this part introduces a number of new imaging agents which were potential value of clinical applications. In the third part, the clinical application of the conventional imaging agent 18F-FDG in different tumors and neurodegenerative diseases and 18F-Dopa imaging in the nervous system are discussed. Besides the clinical applications of 99mTc labeled radiopharmaceuticals in parathyroid disease, coronary heart disease, myocardial infarction, sentinel lymph node, metastatic bone tumors, liver and gallbladder disease in children are introduced. Finally, the applications of radionuclide 131I on treatments of Graves' disease and differentiated thyroid cancer and metastases are investigated respectively. This book is a useful reference for professionals engaged in nuclear medicine and clinical research, including clinical nuclear medicine physicians, nuclear medicine engineers and nuclear medicine pharmacists. |
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is …
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
artificial - Wiktionary, the free dictionary
6 days ago · artificial (comparative more artificial, superlative most artificial) Man-made; made by humans; of artifice. The flowers were artificial, and he thought them rather tacky. An artificial …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real …
Artificial Intelligence Is Not Intelligent - The Atlantic
Jun 6, 2025 · The good news is that nothing about this is inevitable: According to a study released in April by the Pew Research Center, although 56 percent of “AI experts” think artificial …
ARTIFICIAL Definition & Meaning - Merriam-Webster
The meaning of ARTIFICIAL is made, produced, or done by humans especially to seem like something natural : man-made. How to use artificial in a sentence.
ARTIFICIAL | English meaning - Cambridge Dictionary
ARTIFICIAL definition: 1. made by people, often as a copy of something natural: 2. not sincere: 3. made by people, often…. Learn more.
Artificial - definition of artificial by The Free Dictionary
1. produced by man; not occurring naturally: artificial materials of great strength. 2. made in imitation of a natural product, esp as a substitute; not genuine: artificial cream. 3. pretended; …
ARTIFICIAL Definition & Meaning | Dictionary.com
Artificial is used to describe things that are made or manufactured as opposed to occurring naturally. Artificial is often used as the opposite of natural. A close synonym of artificial is synthetic.
ARTIFICIAL definition and meaning | Collins English Dictionary
Artificial objects, materials, or processes do not occur naturally and are created by human beings, for example using science or technology.
artificial adjective - Definition, pictures, pronunciation and usage ...
Definition of artificial adjective in Oxford Advanced Learner's Dictionary. Meaning, pronunciation, picture, example sentences, grammar, usage notes, synonyms and more.
Artificial - Definition, Meaning & Synonyms - Vocabulary.com
While artificial can simply mean “made by humans,” it’s often used in a negative sense, conveying the idea that an artificial product is inferior to the real thing. If you remark that your friend’s new …
artificial - Wiktionary, the free dictionary
6 days ago · artificial (comparative more artificial, superlative most artificial) Man-made; made by humans; of artifice. The flowers were artificial, and he thought them rather tacky. An artificial …
What does artificial mean? - Definitions.net
Artificial refers to something that is made or produced by human beings rather than occurring naturally or in the environment. It often implies an imitation of something natural or a real version, …
Artificial Intelligence Is Not Intelligent - The Atlantic
Jun 6, 2025 · The good news is that nothing about this is inevitable: According to a study released in April by the Pew Research Center, although 56 percent of “AI experts” think artificial …
