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Developing the Digital Lung,

Edition 1 From First Lung CT to Clinical AI

Editors: By John D. Newell, MD FACR

Publication Date: 20 Dec 2022

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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.

Key Features

Traces the development of precise quantitative CT of diffuse lung disease through the use of applied AI, leading to faster effective diagnosis of patients with lung disease.
Reviews CT manufacturers, models and scanning protocol used to produce the 3D digital maps of the lungs.
Discusses how the data processed by AI algorithms can produce measures of emphysema, air trapping, and airway wall thickening in subjects with COPD and measures of pulmonary fibrosis and traction bronchiectasis in idiopathic pulmonary fibrosis (IPF).
Demonstrates the differences between reactive machine AI and limited memory AI methods.
Includes comprehensive case studies and current information on cloud computing.
An eBook version is included with purchase. The eBook allows you to access all of the text, figures and references, with the ability to search, customize your content, make notes and highlights, and have content read aloud.

About the author

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

1 Introduction to Lung CT AI
2 Three-Dimensional (3D) Digital Images of the Lung Using X-ray Computed Tomography
3 X-ray CT Scanning Protocols for Lung CT AI Applications
4 Quantitative Assessment of Lung Nodule Size, Shape, and Malignant Potential Using Both Reactive and Limited-Memory Lung CT AI
5 Using Reactive Machine AI to Derive Quantitative Lung CT Metrics of COPD, ILD, and COVID-19 Pneumonia
6 Using Reactive Machine AI and Dynamic Changes in Lung Structure to Derive Functional Quantitative Lung CT Metrics of COPD, ILD, and Asthma
7 Using Limited Memory Lung CT AI to Derive Advanced Quantitative CT Lung Metrics of COPD, ILD, and COVID-19 Pneumonia
8 Lung CT AI Enables Advanced Computer Modeling of Lung Physiome Structure and Function
9 Adoption of Lung CT AI Into Clinical Medicine
Index

Developing the Digital Lung,

Editors: By John D. Newell, MD FACR

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About the author