Chapter 9 - Adoption of Lung CT AI Into Clinical Medicine

Abstract

The previous eight chapters have been in preparation of this chapter. One of the main goals of this book is to show the reader how the advancement of lung CT AI over the past 45 years has made it possible to provide lung CT AI technologies in the current clinical medical imaging environment. This chapter will first describe the current information technologies (IT) that are in use in modern healthcare hospitals and clinics to support the acquisition, storage, and distribution of medical imaging studies including x-ray CT studies of the thorax, and how imaging IT interacts with the larger healthcare IT that supports the patient’s electronic medical record (EMR). Then we will discuss the lung CT AI technology currently available within the imaging IT ecosystem to automatically assess lung CT images for quantitative metrics of lung disease. Specifically, we will discuss VIDA Insights v3.0 Density/tMPR module and Texture/Subpleural View module that use reactive machine AI and limited-memory AI methods to analyze each lung and lobe of a chest CT scan for the following quantitative CT metrics: volume in liters, LAA−950 metric for emphysema, HAA−700 to −250 metric for COVID-19 pneumonia and ILD, and the texture patterns that make up the HAA−700 to −250 that include ground-glass/reticular opacities, consolidation, and honeycombing. The importance of responsible AI will be discussed along with guidelines to achieve responsible lung CT AI.