LIMITED OFFER
Save 50% on book bundles
Immediately download your ebook while waiting for your print delivery. No promo code is needed.
Developing the Digital Lung
From First Lung CT to Clinical AI
- 1st Edition - September 13, 2022
- Author: John D. Newell
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 7 9 5 0 1 - 2
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 7 9 5 0 2 - 9
Reflecting recent major advances in the field of artificial intelligence, Developing the Digital Lung, From First Lung CT to Clinical AI, by Dr. John Newell, is your go-to… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteReflecting recent major advances in the field of artificial intelligence, Developing the Digital Lung, From First Lung CT to Clinical AI, by Dr. John Newell, is your go-to reference for all aspects of applied artificial intelligence in lung disease development, including application to clinical medicine. It provides a unique overview of the field, beginning with a review of the origins of artificial intelligence in the mid-1970s and progressing to its application to clinical medicine in the early 2020s. Organized based on the four stages of development, this practical, easy-to-use resource helps you effectively apply artificial intelligences to lung imaging.
- Cover image
- Title page
- Table of Contents
- Any screen, Any time, Anywhere
- Copyright
- Dedication
- Acknowledgments
- Preface
- Chapter 1. Introduction to Lung CT AI
- Abstract
- AI: An Intelligent Agent
- Diagnosis of COPD, ILD, Lung Cancer, and Other Smoking-Related Diseases
- Information for Healthcare Providers and Administrators, Patients, and Researchers
- Describing Lung CT AI in Three Stages
- References
- Chapter 2. Three-Dimensional (3D) Digital Images of the Lung Using X-ray Computed Tomography
- Abstract
- The Digital Lung
- X-ray Computed Tomography
- CT Scanning Protocols
- X-ray CT Radiation Dose
- Brief History of X-ray CT
- References
- Chapter 3. X-ray CT Scanning Protocols for Lung CT AI Applications
- Abstract
- Early Work in the Development of QCT Scanning Protocols
- Current Recommended Quantitative CT Scanning Protocol
- CT Scanner Quality Control
- Current QIBA Lung Density CT Profile
- Summary
- References
- Chapter 4. Quantitative Assessment of Lung Nodule Size, Shape, and Malignant Potential Using Both Reactive and Limited-Memory Lung CT AI
- Abstract
- CT Assessment of Lung Nodules—CT Versus Projection Radiography (PR)
- CT Determination of Lung Nodule Size
- CT Determination of Nodule Growth
- CT Determination of Nodule Density
- CT Determined Nodule Mass, Location, Morphology, Shape, Contour
- CT Determined Nodule Texture—Limited-Memory AI
- CT Assessment of Lung Tissue Adjacent to the Lung Nodule—Limited-Memory AI
- References
- Chapter 5. Using Reactive Machine AI to Derive Quantitative Lung CT Metrics of COPD, ILD, and COVID-19 Pneumonia
- Abstract
- Introduction
- Normal Lung Structure
- QCT Scanning Protocol and Lung Segmentation
- Chronic Obstructive Pulmonary Disease (COPD) Induced Changes in Lung Structure
- Clinical Value of Using Lung CT AI in Patients with Environmental Exposure to Cigarette Smoke
- Interstitial Lung Disease (ILD) Induced Changes in Lung Structure
- QCT of COVID-19 Acute Viral Pneumonia
- Summary
- References
- Chapter 6. Using Reactive Machine AI and Dynamic Changes in Lung Structure to Derive Functional Quantitative Lung CT Metrics of COPD, ILD, and Asthma
- Abstract
- Introduction
- Expiratory QCT Assessment of Air Trapping Due to Small Airway Disease in the Lung
- Assessment of Air Trapping at the Voxel Level Using Image Registration
- Assessment of Biomechanics and Tissue Stiffness Using Image Registration
- Direct Measurements of Large Airway Geometry Using Lung CT AI
- Summary
- References
- Chapter 7. Using Limited Memory Lung CT AI to Derive Advanced Quantitative CT Lung Metrics of COPD, ILD, and COVID-19 Pneumonia
- Abstract
- Introduction
- Limited Memory Lung CT AI and the Assessment of Emphysema
- Limited Memory Lung CT AI and the Assessment of Interstitial Lung Disease (ILD)
- CNN for COVID-19 Pneumonia
- Summary
- References
- Chapter 8. Lung CT AI Enables Advanced Computer Modeling of Lung Physiome Structure and Function
- Abstract
- Virtual Physiological Human and a Lung Physiome Model
- Finite Element Model of Lung Structure and Function
- Lung Physiome (LP) Model Applied to the Assessment of Acute Pulmonary Embolism
- Summary of Important Concepts of the Lung Physiome Model
- References
- Chapter 9. Adoption of Lung CT AI Into Clinical Medicine
- Abstract
- Introduction
- Healthcare Imaging IT
- Electronic Medical Record (EMR)
- Clinical Lung CT AI Software
- Responsible AI
- References
- Index
- Confidence is ClinicalKey
- No. of pages: 160
- Language: English
- Edition: 1
- Published: September 13, 2022
- Imprint: Elsevier
- Paperback ISBN: 9780323795012
- eBook ISBN: 9780323795029
JN
John D. Newell
Dr John D Newell Jr MD FACR is a Visiting Professor of Radiology and Biomedical Engineering at the University of Iowa and the Medical Advisor to VIDA, a lung CT AI software company. He has over 40 years of experience in CardioThoracic Radiology and Biomedical Engineering. He has been researching and publishing in the area of lung CT AI for many years and wants to help translate the research done in lung CT AI into the clinical care of patients with lung disease. He lives in Port Townsend, Washington.
Affiliations and expertise
University of Colorado, Health Sciences Center, Denver, CO, USARead Developing the Digital Lung on ScienceDirect