Description
Book SynopsisThis book includes detailed explanations of the underlying technologies and concepts used in Artificial Intelligence (AI) and Machine Learning (ML) in the context of nuclear medicine and hybrid imaging. A diverse team of authors, including pioneers in the field and respected experts from leading international institutions, share their insights, opinions and outlooks on this exciting topic.
A wide range of clinical applications are discussed, from brain applications to body indications, as well as the applicability of AI and ML for cardio-vascular conditions. The book also considers the potential impact of theranostics. To balance the technology-heavy and disease-specific applications, it also discusses ethical / legal issues, economic realities and the human factor, the physician. Though this discussion is not based on research and outcomes, it provides important insights into the ramifications of how AI and ML could transform Nuclear Medicine and Hybrid Imaging practice.
As the first work highlighting the role of these concepts specifically in this field, rather than for medical imaging in general, this book offers a valuable resource for Nuclear Medicine Physicians, Radiologists, Physicists, Medical Imaging Administrators and Nuclear Medicine Technologists alike.
Table of ContentsPART I: INTRODUCTION
Editorial: Benefits and challenges of AI/ML in hybrid imaging and molecular imaging
P. Veit-Haibach and Ken Herrmann
PART II: TECHNOLOGY
- Role and influence of AI/ML in healthcare and specifically hybrid imaging and molecular imaging
(Benjamin L. Franc and Guido Davidzon)
- Radiomics, Radiogenomics, AI, Deep-Learning and Machine Learning - Definitions and Imaging Applications (Jens Kleesiek, Germany)
- Radiomics in Nuclear Medicine - Robustness, Reproducibility, Standardisation
(Reza Rezai, Toronto)
- Evolution of AI/ML in molecular imaging – we did this before
- AI based applications in Hybrid Imaging – how to build smart and truly multi-parametric decision models
- Basic principles of neural networks: types, applications and “usefulness” for molecular imaging
(Josh Kaggie, Cambridge, UK, Chitresh Bhushan, GE, Niskayuna, NY, US and Dawei Gui, GE, Waukesha, Wi, US)
PART III CLINICAL APPLICATIONS
Imaging biomarkers and their meaning for molecular imaging (Angel Alberich Bayarri, Valencia, Spain) Integration of AI/ML in Clinical Routine in Molecular Imaging - Structured reporting with AI/ML in Molecular Imaging in Theranostics
- Imaging biobanks for molecular imaging – how to integrate ML/AI into our databases Angel Alberich Bayarri, Valencia, Spain)
- AI/ML Imaging applications in head and neck diseases (neuro/onco)
(Mijin Jun, Seoul, Korea for neurodegenerative applications)
- AI/ML Imaging applications in body oncology (Robert Seifert et al, Muenster, Germany)
- AI/ML imaging applications in cardiac imaging (Piotr Slomka, LA, US)
AI/ML Imaging applications in therapy follow up and therapy decision support
PART IV: Impact of A.I. and ML on Molecular Imaging and Theranostics
- AI/ML will help to improve Molecular Imaging as well as the Molecular Therapy/Theranostic – what are the biggest advantages for Imaging and Therapy?. (Benjamin L. Franc and ?)
Why imaging data is not enough – AI based integration of imaging and clinical data Legal and ethical issues in AI/ML – why does the patient does not own his /her data? (Prainsack, Vienna)
- The role of A.I./ML for clinical trials in molecular imaging
- Show me the money – investor landscape in AI/ML in molecular imaging and therapy
Physician centered imaging interpretation is dying out - why should I be a radiologist/nuclear medicine physician or how do we attract the smartest people to our field (Roland Hustinx, Liege, Belgium) - Advantages and risks of A.I./ML for Molecular Imaging Specialists – what do we need to learn to master the “beast”
