Carlo N. De Cecco – författare

This book provides an overview of current and potential applications of artificial intelligence (AI) for cardiothoracic imaging. Most AI systems used in medical imaging are data-driven and based on supervised machine learning. Clinicians and AI specialists can contribute to the development of an AI system in different ways, focusing on their respective strengths. Unfortunately, communication between these two sides is far from fluent and, from time to time, they speak completely different languages. Mutual understanding and collaboration are imperative because the medical system is based on physicians’ ability to take well-informed decisions and convey their reasoning to colleagues and patients.This book offers unique insights and informative chapters on the use of AI for cardiothoracic imaging from both the technical and clinical perspective. It is also a single comprehensive source that provides a complete overview of the entire process of the development and use of AI in clinical practice for cardiothoracic imaging. The book contains chapters focused on cardiac and thoracic applications as well more general topics on the potentials and pitfalls of AI in medical imaging. Separate chapters will discuss the valorization, regulations surrounding AI, cost-effectiveness, and future perspective for different countries and continents. This book is an ideal guide for clinicians (radiologists, cardiologists etc.) interested in working with AI, whether in a research setting developing new AI applications or in a clinical setting using AI algorithms in clinical practice. The book also provides clinical insights and overviews for AI specialists who want to develop clinically relevant AI applications.

This book provides an overview of current and potential applications of artificial intelligence (AI) for cardiothoracic imaging. Most AI systems used in medical imaging are data-driven and based on supervised machine learning. Clinicians and AI specialists can contribute to the development of an AI system in different ways, focusing on their respective strengths. Unfortunately, communication between these two sides is far from fluent and, from time to time, they speak completely different languages. Mutual understanding and collaboration are imperative because the medical system is based on physicians’ ability to take well-informed decisions and convey their reasoning to colleagues and patients.This book offers unique insights and informative chapters on the use of AI for cardiothoracic imaging from both the technical and clinical perspective. It is also a single comprehensive source that provides a complete overview of the entire process of the development and use of AI in clinical practice for cardiothoracic imaging. The book contains chapters focused on cardiac and thoracic applications as well more general topics on the potentials and pitfalls of AI in medical imaging. Separate chapters will discuss the valorization, regulations surrounding AI, cost-effectiveness, and future perspective for different countries and continents. This book is an ideal guide for clinicians (radiologists, cardiologists etc.) interested in working with AI, whether in a research setting developing new AI applications or in a clinical setting using AI algorithms in clinical practice. The book also provides clinical insights and overviews for AI specialists who want to develop clinically relevant AI applications.

This book provides an overview of current and potential applications of artificial intelligence (AI) for cardiothoracic imaging. Most AI systems used in medical imaging are data-driven and based on supervised machine learning. Clinicians and AI specialists can contribute to the development of an AI system in different ways, focusing on their respective strengths. Unfortunately, communication between these two sides is far from fluent and, from time to time, they speak completely different languages. Mutual understanding and collaboration are imperative because the medical system is based on physicians’ ability to take well-informed decisions and convey their reasoning to colleagues and patients.This book offers unique insights and informative chapters on the use of AI for cardiothoracic imaging from both the technical and clinical perspective. It is also a single comprehensive source that provides a complete overview of the entire process of the development and use of AI in clinical practice for cardiothoracic imaging. The book contains chapters focused on cardiac and thoracic applications as well more general topics on the potentials and pitfalls of AI in medical imaging. Separate chapters will discuss the valorization, regulations surrounding AI, cost-effectiveness, and future perspective for different countries and continents. This book is an ideal guide for clinicians (radiologists, cardiologists etc.) interested in working with AI, whether in a research setting developing new AI applications or in a clinical setting using AI algorithms in clinical practice. The book also provides clinical insights and overviews for AI specialists who want to develop clinically relevant AI applications.

The aim of this book is fourfold: to describe the basic principles of Dual Energy CT (DECT) imaging, to explain how post-processing is utilized, to critically appraise the strengths and weaknesses of the technique in oncologic imaging, and above all, to demonstrate the contribution of DECT in the management of oncologic patients. Information is provided on the practice and interpretation of DECT and its impact on clinical decision making in a variety of oncological settings, including tumors of the head and neck, lung, liver, pancreas, gastrointestinal system, kidney, and musculoskeletal system.DECT is an innovative imaging technique in which the application of two distinct energy settings provides a transition from density-based image to spectral imaging.As detailed in this book, DECT acquisition allows the simultaneous generation of multiple datasets which enable the radiologist to address varied diagnostic problems in oncologic imaging using a multiparametric approach, having clear advantages in tumor detection, lesion characterization and evaluation of treatment response.