DEVELOPING THE DIGITAL LUNG, FROM FIRST LUNG CT TO CLINICAL AI

DEVELOPING THE DIGITAL LUNG, FROM FIRST LUNG CT TO CLINICAL AI

Editorial:
ELSEVIER UK
Año de edición:
Materia
Radiología
ISBN:
978-0-323-79501-2
Páginas:
160
N. de edición:
1
Idioma:
Inglés
Disponibilidad:
Disponible en 10 días

Descuento:

-5%

Antes:

89,00 €

Despues:

84,55 €

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

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 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.

Author
John D. Newell, MD FACR, University of Colorado, Health Sciences Center, Denver, CO, USA.