Adoption of Lung CT AI Into Clinical Medicine


The successes in the 2000s and 2010s in developing reactive machine AI and limited-memory AI methods to detect and assess the present and severity of x-ray chest CT imaging findings associated with COVID-19 pneumonia, COPD, ILD, and lung cancer have spurred the development of multiple quantitative CT (QCT) lung AI companies, such as VIDA, that offer point-of-care lung CT AI products to assess lung diseases. VIDA’s specialized FDA-approved lung CT AI program, VIDA Insights v3.0, can be run independently or inside a larger medical imaging AI ecosystem. It is now possible for every chest CT scan to be analyzed in near real time for QCT AI metrics of COVID-19 pneumonia, COPD, ILD, and lung nodules. The lung CT AI information is automatically generated and inserted in imaging physicians’ reports and have an immediate impact on the detection and assessment of the severity of diffuse lung disease and lung cancer. It has been a long journey from the mid-1970s to the early 2020 s, but QCT AI of lung disease for the clinical care and treatment of COPD and ILD is now coming of age.

Healthcare Imaging IT

Imaging technology in modern healthcare systems relies on an ecosystem of AI agents to deliver high-quality medical care to patients. The AI agents covered in this chapter include the electronic medical record, radiology information system, picture archiving and communication, voice recognition and reporting, and disease-specific quantitative lung CT AI agents.

Electronic Medical Record (EMR)

The EMR is an AI program that stores, transmits, and displays critical medical information for a patient seen within a healthcare organization (e.g., clinic, hospital, web). The EMR is used by medical personnel to care for patients. The EMR is interfaced with other AI programs that perform more specialized AI functions, such as medical imaging. The medical imaging AI programs include picture archiving and communication (PACS) software programs, radiology information system (RIS) software programs, and voice recognition and reporting (VR) software programs. The PACS, RIS, and VR programs all work together to enable imaging physicians to create imaging reports on the medical imaging studies contained in the PACS and then have these same imaging reports sent to the EMR where they can be viewed by the ordering physician and the other healthcare workers and the patient.

Picture Archiving and Communication System (PACS)

The PACS is the core technology that is responsible for storing, transmitting, and displaying medical images within healthcare systems. The major PACS components include hardware, software, and local area networks ( Fig. 9.1 ). The hardware consists of computer servers to store and transmit medical imaging studies, computer workstations where the medical images are interpreted by imaging physicians, and local area networks that connect the medical imaging devices (e.g., x-ray CT scanners) to the computer servers that store the medical imaging data. The PACS software components include the software that controls the storage and transmission of medical images, software to display and interact with the medical images, and software that exchanges information with the RIS and EMR.

Fig. 9.1, Computer network relationships between different medical informatics technologies including the CT scanner, Radiologist Workstation, PACS Server/Archive, HL Interface, and RIS.

The data format for medical images has been standardized using what is called the Digital Imaging and Communications in Medicine (DICOM) standard. DICOM provides standard protocols for exchanging and storing medical image data including both image data and text associated with the image data. Manufacturers of PACS software have adopted the DICOM standard. The Medical and Imaging Technical Alliance (MITA) division of the National Electrical Manufacturers Association (NEMA) manages a structured document describing the DICOM standard. The DICOM Standard’s document currently includes 20 Parts, Parts 1–8, Parts 10–12, and Parts 14–22. Each of the DICOM Standard Parts address specific subject areas, for example Part 10 addresses “Media Storage and File Format for Media Interchange”. The International Organization for Standardization has recognized DICOM as the ISO 10252 standard.

The development of web-based PACS software has driven the need to develop an application programming interface (API) for handling DICOM-compliant medical imaging data on the worldwide web. DICOMweb has been developed to provide an API to support DICOM standards for web applications similar to what DICOM standards have done for PACS systems. The DICOMweb API provides software programming standards for web-based medical imaging applications for sending, retrieving, and querying images and image-related information.

Current PACS systems provide for “hanging protocols” to be assigned to each type of imaging study. Hanging protocols are implemented within the DICOM standard. The term “hanging” comes from the historic method of hanging hardcopy film on a viewer for the imaging physician to visually interpret and report the findings of a given imaging study. The PACS hanging protocols provide powerful tools to organize the display of the different imaging studies, such as chest CT scans. The hanging protocols are not specific to a patient but are specific to the type of imaging study performed. The chest CT hanging protocol can include multiple parameters including anatomic laterality (right versus left), the anatomic plane that is displayed (e.g., axial, coronal, sagittal), reconstruction method (FBP versus IR), reconstruction kernel, slice thickness, slice interval, window width (WW), window level (WL), location on the displays of the current chest CT images, and historic chest CT’s if they exist. A lung CT AI program that produces additional DICOM outputs needs to be able to integrate into the hanging protocol of the different PACS vendor systems. This is usually done by generating a new CT series that can be handled by the PACS like any other CT imaging series.

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