Decision Support Systems in Diuresis Renography

Abstract

The volume of diagnostic imaging studies performed in the United States is rapidly increasing resulting from an increase in the number of patients as well as an increase in the volume of studies per patient. Concurrently, the number and complexity of images in each patient data set are also increasing. Nuclear medicine physicians and radiologists are required to master an ever-expanding knowledge base whereas the hours available to master this knowledge base and apply it to specific tasks are steadily shrinking. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. The problem is particularly acute for lowvolume studies such as MAG3 diuresis renography where many imagers may have had limited training or experience. To address this problem, renal decision support systems (DSS) are being developed to assist physicians evaluate suspected obstruction in patients referred for diuresis renography. Categories of DSS include neural networks, case-based reasoning, expert systems and statistical systems; RENEX and CART are examples of renal DSS currently in development. RENEX (renal expert) uses a set of rules obtained from human experts to analyze a knowledge base of expanded quantitative parameters obtained from diuresis MAG3 scintigraphy whereas CART (classification and regression tree analysis) is a statistical method that grows and prunes a decision tree based on an analysis of these quantitative parameters in a training data set. RENEX can be queried to provide the reasons for its conclusions. Initial data show that the interpretations provided by RENEX and CART are comparable to the interpretations of a panel of experts blinded to clinical information. This project should serve as a benchmark for the scientific comparison and collaboration of these 2 fields of medical decision-making. Moreover, we anticipate that these DSS will better define the essential interpretative criteria, foster standardized interpretation, teach trainees to better interpret renal scans, enhance diagnostic accuracy and provide a methodology applicable to other diagnostic problems in radiology and medicine.The number of diagnostic imaging studies performed annually in the United States is rapidly increasing. In part, this increase is the result of an increase in population but an even more important factor is an increase in the number of studies performed for each patient. The volume of imaging procedures for each Medicare beneficiary, for example, has been increasing about 10% per year for the past 5 years. 1 At the same time the volume of imaging studies is increasing, the number and complexity of the images in each patient data set is also increasing. Imagers are required to master an ever-expanding knowledge base whereas the hours available to master this knowledge base and apply it to specific tasks (selecting the most appropriate protocol, quality control, image interpretation, reporting) are steadily shrinking. The convergence of an expanding knowledge base...

Overall indices for assessing agreement among multiple raters

“…For example, data from a renal study (details are given in Section 4) demonstrate the need for such a statistical framework. In the absence of a gold standard, it is generally accepted that the best available interpretation of renal scans comes from experienced experts, but interobserver variability still exists as their interpretations do not always agree with each other . Practicing radiologists at US hospitals often have marked variability in their interpretations compared to experienced readers due to the fact that their training in nuclear medicine was limited to 3‐4 months .…”

Computer-Assisted Diagnosis in Renal Nuclear Medicine: Rationale, Methodology, and Interpretative Criteria for Diuretic Renography

“…Our approach has been similar; we have used the two stage acquisition since 1990 and omitted Stage 2 when the baseline acquisition was normal and excluded obstruction. The details of our acquisition and processing protocols have been described in previous publications (35, 36, 40). …”

Decision support systems for clinical radiological practice — towards the next generation

“…Rule-based expert systems in radiology RENEX [13], an ES for the diagnosis of renal obstruction from nuclear medicine scans, has been seen to perform at a level approximating that of a domain expert and is subject to further testing [14].…”

“…Rule-based expert systems in radiology RENEX [13], an ES for the diagnosis of renal obstruction from nuclear medicine scans, has been seen to perform at a level approximating that of a domain expert and is subject to further testing [14]. The explanatory systems in this and other ES are of particular benefit as training tools by explaining the importance of findings to clinicians who do not hold the domain experience to understand the implicit knowledge inferred from the system's recommendations [15][16][17].…”

“…For any system to work effectively it must assimilate the knowledge and decision-making framework of the domain expert, as well as integrating new knowledge, and display this knowledge in a form that is easy to visualise and understand. In other words, the system should be able to explain how it has reached its particular conclusion.Rule-based expert systems in radiology RENEX [13], an ES for the diagnosis of renal obstruction from nuclear medicine scans, has been seen to perform at a level approximating that of a domain expert and is subject to further testing [14]. …”