Computer-assisted diagnosis in renal nuclear medicine: rationale, methodology, and interpretative criteria for diuretic renography.
The goal of artificial intelligence, expert systems, decision support systems, and computer-assisted diagnosis (CAD) in imaging is the development and implementation of software to assist in the detection and evaluation of abnormalities, to alert physicians to cognitive biases, to reduce intraobserver and interobserver variability, and to facilitate the interpretation of studies at a faster rate and with a higher level of accuracy. These developments are needed to meet the challenges resulting from a rapid increase in the volume of diagnostic imaging studies coupled with a concurrent increase in the number and complexity of images in each patient data. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. Errors are even more likely when physicians interpret low-volume studies such as technetium-99m-mercaptoacetyltriglycine diuretic scans where imagers may have had limited training or experience. Decision support systems include neural networks, case-based reasoning, expert systems, and statistical systems. iRENEX (renal expert) is an expert system for diuretic renography that uses a set of rules obtained from human experts to analyze a knowledge base of both clinical parameters and quantitative parameters derived from the renogram. Initial studies have shown that the interpretations provided by iRENEX are comparable to the interpretations of a panel of experts. iRENEX provides immediate patient-specific feedback at the time of scan interpretation, can be queried to provide the reasons for its conclusions, and can be used as an educational tool to teach trainees to better interpret renal scans. It also has the capacity to populate a structured reporting module and generate a clear and concise impression based on the elements contained in the report; adherence to the procedural and data entry components of the structured reporting module ensures and documents procedural competency. Finally, although the focus is CAD applied to diuretic renography, this review offers a window into the rationale, methodology, and broader applications of computer-assisted diagnosis in medical imaging.
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA. Electronic address: ataylor@emory.edu.
Andrew T
AT
Taylor
Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA.
Ernest V
EV
Garcia
D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004232:Diuretics / Q000494:pharmacology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging* / Q000187:drug effects*; D009683:Nuclear Medicine / Q000379:methods*; D011866:Radioisotope Renography / Q000379:methods*
D004232:Diuretics