"Frameworks for Radiology Reporting outlines methodical systems to aid image analysis for commonly encountered radiological examinations. These systems will help facilitate a reproducible process of image interpretation and avoid the common pitfalls of reporting. The book will not only be of value to trainee radiologists but also to physicians and radiographers with an interest in the process of image interpretation." --Book Jacket.
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.
The World Health Organization stated that approximately two-thirds of the world’s population lacks adequate access to medical imaging. The scarcity of imaging services in developing regions contributes to a widening disparity of health care and limits global public health programs that require imaging. Radiology is an important component of many global health programs, including those that address tuberculosis, AIDS-related disease, trauma, occupational and environmental exposures, breast cancer screening, and maternal-infant health care. There is a growing need for medical imaging in global health efforts and humanitarian outreach, particularly as an increasing number of academic, government, and non-governmental organizations expand delivery of health care to disadvantaged people worldwide. To systematically deploy clinical imaging services to low-resource settings requires contributions from a variety of disciplines such as clinical radiology, epidemiology, public health, finance, radiation physics, information technology, engineering, and others. This book will review critical concepts for those interested in managing, establishing, or participating in a medical imaging program for resource-limited environments and diverse cross-cultural contexts undergoing imaging technology adaptation.
In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions.
As advanced practices and role extension within the healthcare sector continues unabated, increasingly practitioners seek ways to widen their professional remit and develop and add to their skills. Interpreting Trauma Radiographs provides a unique guide to enable radiographers and trained healthcare professionals to confidently and competently interpret and report on radiographic images. Designed specifically for radiographers, casualty (accident and emergency) medical officers and trainees, and other health professionals who regularly encounter trauma radiography as part of their work, this book brings together expert contributions on the clinical, medical, legal and scientific aspects of radiographic interpretation and reporting, promoting a thorough understanding of both the general framework of reporting and the detail of image interpretation. The book is divided into two sections. The first section deals with the overall framework of image reporting and interpretation: the radiologist’s perspective, the legal aspects, scientific background and the psychological nature of perception and interpretation. The second section focuses on image interpretation of regional anatomy, presented to support both reporting practitioners in training and those more experienced in reporting practice. Interpreting Trauma Radiographs is an invaluable companion for qualified radiographers, radiographers in training, casualty medical officers, and other healthcare professionals, such as nurse practitioners, aspiring to interpret and report on radiographic images.
This highly practical text is aimed at surgeons – both consultants and those in training who are interested in the advancing role played by imaging technology within surgical decision making. The first part of the book describes the principles of imaging, and the different imaging techniques available to the surgeon. The second part is symptom-based rather than organ-based, with the aim of providing a practical hands-on approach to imaging patients with common surgical complaints. Helpful bullet-points will assist the surgeon to better understand the imaging options available to them, and choose the correct modalities using a problem-based approach.
This book provides practitioners and scientists with insights into diverse aspects of structured reporting to allow them to develop tools and a knowledge base to ensure that this electronic reporting trend is widely applied. After an introduction to reporting in radiology, various parts of structured reporting are discussed in detail, including an overview of standardized reporting systems, standardized reporting language, DICOM structured reporting, template based structured reporting, and modular reporting. The last chapter addresses the interaction of structured reporting with artificial intelligence and its impact on the future of radiology. The last chapter addresses the interaction of structured reporting with artificial intelligence and its impact on the future of radiology. Endorsed by the European Society of Medical Imaging Informatics (EuSoMII), the scope of the book is based on the Medical Imaging Informatics sub-sections of the European Society of Radiology (ESR) European Training Curriculum Level I and II. It is a valuable resource for residents, radiologists and students.
This book gathers the proceedings of the 21st Engineering Applications of Neural Networks Conference, which is supported by the International Neural Networks Society (INNS). Artificial Intelligence (AI) has been following a unique course, characterized by alternating growth spurts and “AI winters.” Today, AI is an essential component of the fourth industrial revolution and enjoying its heyday. Further, in specific areas, AI is catching up with or even outperforming human beings. This book offers a comprehensive guide to AI in a variety of areas, concentrating on new or hybrid AI algorithmic approaches with robust applications in diverse sectors. One of the advantages of this book is that it includes robust algorithmic approaches and applications in a broad spectrum of scientific fields, namely the use of convolutional neural networks (CNNs), deep learning and LSTM in robotics/machine vision/engineering/image processing/medical systems/the environment; machine learning and meta learning applied to neurobiological modeling/optimization; state-of-the-art hybrid systems; and the algorithmic foundations of artificial neural networks.
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022. The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology; Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging; Part III: Breast imaging; colonoscopy; computer aided diagnosis; Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I; Part V: Image segmentation II; integration of imaging with non-imaging biomarkers; Part VI: Image registration; image reconstruction; Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization; Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
