Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging (Hardcover)

· 제목 : Artificial Intelligence/Machine Learning in Nuclear Medicine and Hybrid Imaging (Hardcover) 
· 분류 : 외국도서 > 의학 > 방사선학/핵의학
· ISBN : 9783031001185
· 쪽수 : 210쪽
· 출판일 : 2022-06-23

PART I: INTRODUCTION

Editorial: Benefits and challenges of AI/ML in hybrid imaging and molecular imaging

P. Veit-Haibach and Ken Herrmann

 

PART II: TECHNOLOGY   

1. Role and influence of AI/ML in healthcare and specifically hybrid imaging and molecular imaging

(Benjamin L. Franc and Guido Davidzon)

1. Radiomics, Radiogenomics, AI, Deep-Learning and Machine Learning - Definitions and Imaging Applications (Jens Kleesiek, Germany)
2. Radiomics in Nuclear Medicine - Robustness, Reproducibility, Standardisation

(Reza Rezai, Toronto)

1. Evolution of AI/ML in molecular imaging ? we did this before

1. AI based applications in Hybrid Imaging ? how to build smart and truly multi-parametric decision models
2. 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

1. Structured reporting with AI/ML in Molecular Imaging in Theranostics

1. Imaging biobanks for molecular imaging ? how to integrate ML/AI into our databases Angel Alberich Bayarri, Valencia, Spain)

1. AI/ML Imaging applications in head and neck diseases (neuro/onco) 

(Mijin Jun, Seoul, Korea for neurodegenerative applications)

1. AI/ML Imaging applications in body oncology (Robert Seifert et al, Muenster, Germany)

1. 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   

1. 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

1. 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)

1. Advantages and risks of A.I./ML for Molecular Imaging Specialists ? what do we need to learn to master the “beast”