Gregory G. Weltin

Transesophageal echocardiographic guidance of transcatheter closure of atrial septal defect

Transcatheter closure of atrial septal defect (ASD) was accomplished in 10 of 11 patients aged 13 months to 46 years (weight range 11 to 77 kg). Transesophageal echocardiography (TEE) was used simultaneously with fluoroscopic imaging in 4 of these patients aged 4.5 to 46 years (weight range 19 to 77 kg). TEE was used to ascertain defect size, position and number of defects and to ascertain appropriate seating of the defect occluder within the atrial defect. In 2 patients TEE-assisted transcatheter ASD closure was accomplished after previous attempts at transcatheter ASD closure, unaided by TEE, had been unsuccessful. The only unsuccessful ASD closure procedure occurred in the smallest patient in the series (an 11-kg 13-month-old), a child who was too small to undergo TEE using our 11-mm diameter endoscopic probe. The concomitant use of TEE with fluoroscopic imaging provides information that is unique and complementary and may improve the efficacy and safety of the transcatheter technique for ASD closure. The recent availability of a 7-mm diameter TEE probe will extend the use of TEE into the infant age group and may decrease the discomfort and potential morbidity of TEE in older patients.

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.

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.

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.