Henry A. Swett

Voice-activated retrieval of mammography reference images

We undertook this project to integrate context sensitive computer-based educational and decision making aids into the film interpretation and reporting process, and to determine the clinical utility of this method as a guide for further system development. An image database of 347 digital mammography images was assembled and image features were coded. An interface was developed to a computerized speech recognition radiology reporting system which was modified to translate reported findings into database search terms. These observations were used to formulate database search strategies which not only retrieved similar cases from the image database, but also other cases that were related to the index case in different ways. The search results were organized into image sets intended to address common questions that arise during image interpretation. An evaluation of the clinical utility of this method was performed as a guide for further system development. We found that voice dictation of prototypical mammographic cases resulted in automatic retrieval of reference images. The retrieved images were organized into sets matching findings, diagnostic hypotheses, diagnosis, spectrum of findings or diagnoses, closest match to dictated case, or user specified parameters. Two mammographers graded the clinical utility of each form of system output. We concluded that case specific and problem specific image sets may be automatically generated from spoken case dictation. A potentially large number of retrieved images may be divided into subsets which anticipate common clinical problems. This automatic method of context sensitive image retrieval may provide a “continuous’; form of education integrated into routine case interpretation.

Hydra A Knowledge Acquisition Tool for Expert Systems That Critique Medical Workup

HYDRA is a computer-based knowledge acquisition tool under development to assist in the creation of expert systems which critique medical workup. To use HYDRA, a domain expert first outlines the recommended approaches to the workup of a chosen medical problem, using the Augmented Transition Network formalism. From this model, HYDRA produces a list of the various conditions for which critiquing comments may be required to react to all possible approaches that might be proposed by the user of the critiquing system. Domain-specific constraints can be used to restrict the number of conditions suggested. In this way, HYDRA assists the domain expert by providing a model for structuring the problem, and by breaking down the domain experts work into a set of small, easily understood tasks.

Optimizing Radiologic Workup: An Artificial Intelligence Approach

The increasing complexity of diagnostic imaging is presenting an ever expanding variety of radiologic test options to clinicians. As a result, it is becoming more difficult for referring physicians to select an appropriate sequence of tests. The current economic pressures on medicine make it particularly important that resources be used judiciously. Radiologic workup often involves a sequence of tests that lead from presenting signs and symptoms to a definitive diagnosis or intervention. This sequence ideally begins with simple, inexpensive, safe, non-invasive tests and progresses to more complex, expensive, and hazardous tests only if the simpler tests are insufficient to establish a diagnosis. DxCON is a developmental artificial intelligence-based computer system that gives advice to physicians about the optimum sequencing of radiologic tests. DxCON evaluates basic clinical information and a physician’s proposed workup plan. The system then creates an analysis of the strengths and weaknesses of his plan. The domain chosen to explore computer-based workup advice is the radiologic workup of obstructive jaundice.

Hypermedia in radiology: Computer-assisted education

Hypertext is a new computer-based method of presenting information that provides greater flexibility than conventional methods of continuing education. With a hypertext system, an individual using the computer can acquire more information on a word or concept that needs to be pursued in depth. Hypermedia is an expanded concept which uses the computer’s ability to incorporate images, sounds, and video images in addition to text. This interactive, multimedia approach customizes information for more effective learning. The authors devised a prototype hypermedia textbook of nuclear medicine using a personal computer with hypermedia software that contains text, graphs, tables, figures, literature citations, and an easily perusable image database. All the information is organized with multiple cross-references, allowing instant branching to relevant facts, in different levels of detail. The system’s applications and the ease of expansion or modification by the user are described.

Critiquing the process of radiologic differential diagnosis

ICON is a developmental expert system designed to critique the process of radiologic differential diagnosis. To use ICON, a physician outlines (1) findings observed in a chest radiograph, (2) a small amount of clinical information describing the patient, and (3) a proposed diagnosis. ICON critiques the appropriateness of that diagnosis in detail, analyzing why and how well the findings serve to confirm it, or to rule it out. ICON may also suggest further information to look for. ICON explores the design issues involved in critiquing the process of differential diagnosis, and is currently implemented in a limited domain: the radiographic diagnosis of a lung mass in a patient with Hodgkins disease.

The Prospect of Expert System-Based Cognitive Support as a By-Product of Image Acquisition and Reporting

In order for computer-based decision-support tools to find routine use in the everyday practice of clinical radiology, further development of user interface and knowledge content are required. In an ideal interface, the interaction between the radiologist and the computer would be minimized and painlessly integrated into existing work patterns. In this article, we explore some of the ways that pre-existing computer interactions in the processes of image acquisition and reporting can be used to feed case information into an expert system and thereby allow users to acquire advice from it in an automatic fashion. We describe interface models that we have developed in the domains of mammography and obstetric ultrasound, and discuss interface and content-related questions that have arisen from informal evaluations of these systems. In particular, the need for clinical outcome-relevant decision support and training level-appropriate decision support are discussed in detail.

Exploring subjective vs. objective issues in the validation of computer-based critiquing advice

Evaluation is an important part of the development of computer-based medical expert systems. Such evaluation may be particularly difficult when judging a critiquing system which responds to a proposed management strategy with a discussion of the advisability of that approach. DxCON is an expert system which produces a prose critique discussing the radiologic workup of obstructive jaundice. This paper describes DxCON, and its experimental validation by three independent judges. A central component of the validation involved allowing the judges to react to the systems advice in a quite flexible, unstructured fashion. This project provides a case study of how subjective issues impact both the design and implementation of a validation of a medical expert system whose output is explanatory prose.

SCAR and SDIMC

THERE is something about an idea whose time has come The Society for Computer Applications in Radiology (SCAR) has been in the planning stages for almost 2 years. We have a growing body of enthusiastic members, a basic organizational structure, and this, the second issue of the Journal ofDigital Imaging. At about the same time that SCAR was conceived, another group of individuals who are actively involved in the development and use of computers in radiology was also planning an organization that became known as The Society for Digital Imaging, Management, and Communication (SDIMC). A number of individuals joined SDIMC and some have joined both SCAR and SDIMC. The almost simultaneous appearance of these two societies is evidence that this is a discipline whose time has come. There is a steadily growing number of individuals who recognize that computers are bringing about some very fundamental changes in radiology. The leadership of SCAR and SDIMC is convinced that we have much to gain by pooling our talents, resources, and enthusiasm, and a great deal to lose by allowing ourselves to be splintered into small groups. We have held a number of meetings over the past year to find a way to unite our two societies. I am pleased to announce that representatives of SCAR and SDIMC have recently reached an agreement to form a single organization and to jointly support Journal ofDigital Imaging as the official journal of the new society. The members of both organizations approved this merger at a

Computer-Aided Diagnosis in Radiology

The amount of knowledge in diagnostic radiology is growing very rapidly and as a result, many practitioners are finding it impossible to practice state-of-the-art radiology. The rapid proliferation of computers and digitally generated images in diagnostic radiology provides an environment well suited to the delivery of computer-based decision support. In this paper, I will describe some of the basic functions and capabilities of contemporary systems and give examples from our research of how they may support radiologic diagnosis for mammography and obstetric ultrasound.

Expert System Advice as a By-Product of Image Acquisition and Reporting: Obstacles to be Overcome

Computer systems are commonplace in radiology today, perhaps more so than in any other branch of medicine. Computers are routinely used to acquire radiological images (e.g. CT, MR, ultrasound, etc.), in radiology information systems, and more recently, in image archiving (PACS) and in film-reporting (computerized voice recognition). The long-term goal of our research has been to extend this use of computers to enhance the actual cognitive processes used in the practice of diagnostic radiology by pairing the strengths of computers (superior memory) with those of humans (superior visual pattern recognition). Specifically, we seek to build computer-based expert systems that can give a radiologist intelligent decision support without intruding on his or her normal film-reading activity and without requiring specific and lengthy computer interaction. To this end we have developed prototype systems that use the computer interactions already being carried out for image acquisition and reporting to acquire case-knowledge about the films being interpreted. This knowledge can then be used by an expert system working in the background to generate intelligent diagnostic advice [1]. The radiologist thus gets the benefit of automatic expert system consultation without having to do anything different from what he or she normally does during the film-reading process.