Artificial Intelligence in Medical Imaging

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 implicationsfor 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.

Provides a thorough overview of the impact of artificial intelligence (AI) on medical imaging Includes contributions from radiologists and IT professionals, ensuring a multidisciplinary approach Makes practical recommendations for the use of AI technology for both clinical and nonclinical applications

Auteur
Dr Erik R. Ranschaert, MD, PhD, is currently radiologist at the ETZ Hospital in Tilburg, the Netherlands, and vice-president of the European Society of Medical Imaging Informatics (EuSoMII). Dr. Ranschaert was trained in radiology at KU Leuven University Hospital in Belgium and graduated in 1994. On July 14th 2016 he was awarded a PhD in Medical Sciences at the University of Antwerp, with a thesis titled: The Impact of Information Technology on Radiology Services. He is certified as Imaging Informatics Professional by the ABII in 2017. He was chairman of the ECR Computer Applications Subcommittee in 2008 and member of the ESR eHealth and informatics subcommittee in 2014 - 2016. He is the first author or co-author of more than 20 peer-reviewed articles and he gave more than 40 lectures on invitation, most topics related to his thesis and imaging informatics.

Sergey Morozov, MD, MPH, PhD is Professor of Radiology and CEO of Radiology Research and Practice Center in Moscow, Russia. Dr. Morozov was trained in clinical imaging at Sechenov Moscow Medical University and clinical effectiveness at Harvard School of Public Health in 2002-2006. He became Chief of Radiology at the Central Clinical Hospital in Moscow in 2007-2012 and then at the European Medical Center in 2013-2015. He is Executive Director of Russian Society of Radiology, President of European Society of Medical Imaging Informatics, past chairman of Imaging Informatics subcommittee of ECR, member of ECR 2019 Program planning committee, RSNA Education Exhibits Awards Committee. He is certified as Imaging Informatics Professional by ABII in 2017. Prof. Dr. Morozov is a renowned expert in clinical imaging, healthcare management and informatics and is the co-author of more than 100 journal articles and 15 book chapters.

Dr Paul Algra MD PhD, Northwest Hospital Group, Alkmaar, The Netherlands, is trained as radiologist in Leiden University Hospital and as neuroradiologist in Free University Amsterdam. His PhD thesis (1992) was on CT and MRI of vertebral metastases. He was vice-president of Dutch Radiological Society. He is member of scientific committee CAR, board member of EuSoMII and editorial board member of several radiology journals. He (co) authored around 50 articles in peer reviewed journals and served as department chief and program director for more than 15 years.

Contenu
PART I: INTRODUCTION: Introduction: Game changers in radiology.- PART II: TECHNIQUES: The role of medical imaging computing, informatics and machine learning in healthcare.- History and evolution of A.I. in medical imaging.- Deep Learning and Neural Networks in imaging: basic principles.- PART III DEVELOPMENT of AI APPLICATIONS: Imaging biomarkers.- How to develop A.I. applications.- Validation of A.I. applications.- PART IV: BIG DATA IN RADIOLOGY: The value of enterprise imaging.- Data mining in radiology.- Image biobanks.- The quest for medical images and data.- Clearance of medical images and data.- Legal and ethical issues in AI.- PART V: CLINICAL USE OF A.I. IN RADIOLOGY: Pulmonary diseases.- Cardiac diseases.- Breast cancer.- Neurological diseases.- PART VI: IMPACT of A.I. on RADIOLOGY: Applications of A.I. beyond image analysis.- Value of structured reporting for A.I..- The role of A.I. for clinical trials.- Market and economy of A.I.: evolution.- The role of an A.I. ecosystem for radiology.- Advantages and risks of A.I. for radiologists.- Re-thinking radiology. <p