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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., (© 2013 Published by Elsevier Inc.)

Kidney obstruction, if untreated in a timely manner, can lead to irreversible loss of renal function. A widely used technology for evaluations of kidneys with suspected obstruction is diuresis renography. However, it is generally very challenging for radiologists who typically interpret renography data in practice to build high level of competency due to the low volume of renography studies and insufficient training. Another challenge is that there is currently no gold standard for detection of kidney obstruction. Seeking to develop a computer-aided diagnostic (CAD) tool that can assist practicing radiologists to reduce errors in the interpretation of kidney obstruction, a recent study collected data from diuresis renography, interpretations on the renography data from highly experienced nuclear medicine experts as well as clinical data. To achieve the objective, we develop a statistical model that can be used as a CAD tool for assisting radiologists in kidney interpretation. We use a Bayesian latent class modeling approach for predicting kidney obstruction through the integrative analysis of time-series renogram data, expert ratings, and clinical variables. A nonparametric Bayesian latent factor regression approach is adopted for modeling renogram curves in which the coefficients of the basis functions are parameterized via the factor loadings dependent on the latent disease status and the extended latent factors that can also adjust for clinical variables. A hierarchical probit model is used for expert ratings, allowing for training with rating data from multiple experts while predicting with at most one expert, which makes the proposed model operable in practice. An efficient MCMC algorithm is developed to train the model and predict kidney obstruction with associated uncertainty. We demonstrate the superiority of the proposed method over several existing methods through extensive simulations. Analysis of the renal study also lends support to the usefulness of our model as a CAD tool to assist less experienced radiologists in the field. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: The first human studies of a characterized radiopharmaceutical containing a {99mTc(CO)3}+ core, Na[99mTc(CO)3(LAN)], demonstrated that Na[99mTc(CO)3(LAN)] was an excellent renal imaging agent; however, its clearance was less than that of 131I-orthoiodohippurate (131I-OIH), and it did not provide a direct measure of effective renal plasma flow. In order to develop a 99mTc renal agent with pharmacokinetic properties equivalent to those of 131I-OIH, we investigated the 99mTc(CO)3/Re(CO)3 complexes formed from carboxymethylmercaptosuccinic acid (CMSAH3) and thiodisuccinic acid (TDSAH4). Once the ligand is bound to 99mTc(CO)3 through a thioether and two carboxyl groups, the complexes have at least one unbound carboxyl group, essential for the interaction with the renal tubular transporter. Methods: X-ray crystal structural analysis of [NMe4][Re(CO)3(CMSAH)] was performed to interpret the nature of 99mTc tracers. CMSAH3 and TDSAH4 were radiolabeled by incubating each ligand and the precursor [99mTc(CO)3(H2O)3]+ at 70°C (pH 7) for 30 min. The products were purified by reversed-phase high-performance liquid chromatography, and biodistribution studies were performed in Sprague–Dawley rats, with 131I-OIH as an internal control at 10 and 60 min. Results: Radiolabeling CMSAH3 and TDSAH4 with the [99mTc(CO)3(H2O)3]+ precursor gave products quantitatively. Analysis of the Re(CO)3 complexes with the CMSAH3 and TDSAH4 ligands demonstrates that ligands are bound in 99mTc/Re(CO)3 complexes through a thioether and two deprotonated carboxyl groups (forming tridentate dianionic moieties, generally with two 5-membered chelate rings). Renal excretion at 60 min (activity in the urine as a percentage of 131I-OIH) was 68±1% for Na3[99mTc(CO)3(TDSA)] but was 98±1% for Na2[99mTc(CO)3(CMSA)]. Conclusion: In rats, Na2[99mTc(CO)3(CMSA)] is extracted by the kidneys and eliminated in the urine almost as rapidly as 131I-OIH; consequently, Na2[99mTc(CO)3(CMSA)] may provide a direct measure of effective renal plasma flow, and further evaluation in humans is warranted. [Copyright &y& Elsevier]