Artificial Intelligence and Image Processing

The evolution of artificial intelligence since the 1950s is discussed, especially as it is being applied in radiology to image processing. Developments in artificial intelligence are now being used to provide a new approach to image processing. Initially, the computer dealt with numeric representations using languages such as FORTRAN and BASIC. Now symbolic languages such as LISP and PROLOG have expanded the use of the computer into nonnumeric symbolic reasoning that is just being applied to image understanding. This paper explains the new languages and their application to image understanding

Cooperation Between a Radiology Computer Consortium and a Computer Manufacturer in the Development of a Radiology Information System

This article reports on the formation of a Radiology Information System Consortium (RISC) by 13 hospitals and medical centers in the United States, including Henry Ford Hospital in Detroit, and the cooperation between this consortium and a major manufacturer of computers and software. Digital Equipment Corporation (DEC), for the common goal of developing a state-of-the-art radiology information system

Distributed Computing in a Hospital Environment

Obtaining appropriate information in a timely fashion in medical practice has always been a burden for the practitioner. In a large hospital which undertakes major computer projects, this burden is intensified because information is not always available in a form directly usable by the physician or support personnel; it is now accumulated on diverse magnetic media where it is moved and processed as electronic pulses. This paper describes a solution which freely allows continued automation at different rates throughout a large hospital while expediting the movement of information where it is needed in a form understandable by the recipient

Digital Radiography: A Review

The fully digital radiology department remains a radiologist\u27s dream. The technology necessary for implementation does not yet exist other than in prototype form. When the technology catches up with the radiologist\u27s ideas, many new capabilities will exist. Electronically stored images will be available for viewing wherever a computer terminal exists. The problem of film loss would be nonexistent. Images could be quickly transmitted for interpretation via microwave networks to sites far removed from where they are acquired. Patient radiation exposure would decrease. Computers would help decrease perception errors and would assist in image interpretation. It may be ten years before a working digital radiology department exists. However, many processes developed toward this end are now gradually being incorporated into radiology departments. One must therefore be familiar with digital imaging. We present a review of the current state of the art in digital radiography. Various methods of image capture are discussed comparing pencil-beam, fan-beam, and area-beam systems. Magnetic tape, digital disk, bubble memory, and other methods of image storage are presented with a brief description of their technical and financial limitations. Teleradiology is also discussed citing current working examples of various systems. An overview of image processing is included

Developing an Integrated Natural Language Database for Gastrointestinal Disease

Using a mainframe computer connected to the Henry Ford Hospital computer network, we developed a database for gastrointestinal disease which includes data from radiologic and endoscopic gastrointestinal examinations, along with corresponding pathologic diagnosis. Because of the large volume of procedures in our practice, we developed several unique features for our system. The user enters data by responding to a series of question-and-answer sets constructed by the clinical staff, who do not have experience in computer programming. Data is stored in a hierarchical format using natural language. Boolean logic is used to retrieve data so that different procedures can be correlated with each other. In addition, several on-line functions permit us to retrieve data on a given patient immediately and provide computer-generated reports. Because the computer is connected to the hospital network, the database can be accessed from various terminals; data also can be transmitted through the network. Research, educational, and quality assurance functions are other applications of the system

Risk of COVID-19 after natural infection or vaccinationResearch in context

Background: While vaccines have established utility against COVID-19, phase 3 efficacy studies have generally not comprehensively evaluated protection provided by previous infection or hybrid immunity (previous infection plus vaccination). Individual patient data from US government-supported harmonized vaccine trials provide an unprecedented sample population to address this issue. We characterized the protective efficacy of previous SARS-CoV-2 infection and hybrid immunity against COVID-19 early in the pandemic over three-to six-month follow-up and compared with vaccine-associated protection. Methods: In this post-hoc cross-protocol analysis of the Moderna, AstraZeneca, Janssen, and Novavax COVID-19 vaccine clinical trials, we allocated participants into four groups based on previous-infection status at enrolment and treatment: no previous infection/placebo; previous infection/placebo; no previous infection/vaccine; and previous infection/vaccine. The main outcome was RT-PCR-confirmed COVID-19 >7–15 days (per original protocols) after final study injection. We calculated crude and adjusted efficacy measures. Findings: Previous infection/placebo participants had a 92% decreased risk of future COVID-19 compared to no previous infection/placebo participants (overall hazard ratio [HR] ratio: 0.08; 95% CI: 0.05–0.13). Among single-dose Janssen participants, hybrid immunity conferred greater protection than vaccine alone (HR: 0.03; 95% CI: 0.01–0.10). Too few infections were observed to draw statistical inferences comparing hybrid immunity to vaccine alone for other trials. Vaccination, previous infection, and hybrid immunity all provided near-complete protection against severe disease. Interpretation: Previous infection, any hybrid immunity, and two-dose vaccination all provided substantial protection against symptomatic and severe COVID-19 through the early Delta period. Thus, as a surrogate for natural infection, vaccination remains the safest approach to protection. Funding: National Institutes of Health