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Pearls and Pitfalls in Pediatric Imaging
This collection of over 90 highly-illustrated case studies explores major and confusing problems in pediatric imaging. All relevant imaging modalities are covered, including ultrasound, conventional radiography, fluoroscopy, CT, MR, Nuclear and Molecular Imaging, and Interventional Radiology. The authors present a strategy for recognizing key information in order to reach an accurate diagnosis, and each case includes differential diagnoses and key teaching points, alerting the reader to common pitfalls in the interpretation of pediatric radiological images. This is a highly valuable resource for trainee pediatric radiologists, and general radiologists who encounter pediatric patients. It will particularly help people preparing for exams, including the core exam, the certifying exam or CAQ exams, as well as pediatric radiologists who want to refresh their knowledge on particular topics. It will also be of interest to pediatricians who wish to improve their diagnostic proficiency and understanding of imaging studies.
Trends of Artificial Intelligence and Big Data for E-Health
This book aims to present the impact of Artificial Intelligence (AI) and Big Data in healthcare for medical decision making and data analysis in myriad fields including Radiology, Radiomics, Radiogenomics, Oncology, Pharmacology, COVID-19 prognosis, Cardiac imaging, Neuroradiology, Psychiatry and others. This will include topics such as Artificial Intelligence of Thing (AIOT), Explainable Artificial Intelligence (XAI), Distributed learning, Blockchain of Internet of Things (BIOT), Cybersecurity, and Internet of (Medical) Things (IoTs). Healthcare providers will learn how to leverage Big Data analytics and AI as methodology for accurate analysis based on their clinical data repositories and c...
Symptomatic
Symptomatic: The Symptom-Based Handbook for Ehlers-Danlos Syndromes and Hypermobility Spectrum Disorders offers a novel approach structured around the panoply of 75 symptoms with which a person with Ehlers-Danlos syndromes (EDS) and hypermobility spectrum disorders (HSD) may present to a clinician. The content is arranged intuitively from head to feet, with each chapter integrating clinical case studies with a concise discussion and two important diagnostic tools: a simplified algorithm for diagnosing and treating each symptom and differential diagnoses and alternative explanations for their symptoms. This is a handbook that combines the expertise of some 70 leading clinicians, representing ...
Artificial Intelligence in Radiology, An Issue of Radiologic Clinics of North America, E-Book
Artificial Intelligence in Radiology, An Issue of Radiologic Clinics of North America, E-Book
AACR 2022 Proceedings: Part A Online-Only and April 10
The AACR Annual Meeting is the focal point of the cancer research community, where scientists, clinicians, other health care professionals, survivors, patients, and advocates gather to share the latest advances in cancer science and medicine. From population science and prevention; to cancer biology, translational, and clinical studies; to survivorship and advocacy; the AACR Annual Meeting highlights the work of the best minds in cancer research from institutions all over the world.
Artificial Intelligence in Medicine
Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticis...
Artificial Intelligence in Medical Imaging
This book, written by authors with more than a decade of experience in the design and development of artificial intelligence (AI) systems in medical imaging, will guide readers in the understanding of one of the most exciting fields today. After an introductory description of classical machine learning techniques, the fundamentals of deep learning are explained in a simple yet comprehensive manner. The book then proceeds with a historical perspective of how medical AI developed in time, detailing which applications triumphed and which failed, from the era of computer aided detection systems on to the current cutting-edge applications in deep learning today, which are starting to exhibit on-p...
