Evaluation of a decision support system for interpretation of myocardial perfusion gated SPECT

Abstract

A decision support system based on neural networks presents interpretations more similar to experienced clinicians compared to a conventional automated quantification software package. This study shows the feasibility of disseminating the expertise of experienced clinicians to less experienced physicians by the use of neural networks.

Area of ischemia assessed by physicians and software packages from myocardial perfusion scintigrams

“…Four different software packages were compared; (EXINI version 5.0beta [11], Emory Cardiac Toolbox version 3.0 (Emory University; ECT [12]), Quantitative Perfusion SPECT version 4.0 (Cedars Sinai; QPS [13]), 4D-MSPECT version 4.0 (Invia Medical Solutions; 4DM [14]). Summed difference scores (SDS) were obtained from all 4 programs and compared to the total possible score from 17 segments (% ischemic myocardium); called EXINI summed difference % (SD%), ECT (SD%), QPS (SD%) and 4DM (SD%) in this article.…”

An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT

“…Lomksy et al have detected that an ensemble of networks were better at simulating physician interpretations than commonly used quantification algorithms (at 90% sensitivity, 85% vs 46% specificity, P \ .001). 27 In our study, internal network parameters were iterated within a predefined range, because their optimum values cannot be determined beforehand. Among the thousands of networks created during training, the best performing ones were selected to form an ensemble in an attempt to obtain a stable network structure each with unique features.…”

Evaluation of new automated gated-SPECT and echocardiographic methods for calculating left ventricular volumes and ejection fraction

“…The CAFU method has been validated, showing a good correlation with QGS, but it produced systematically higher values for EDV, ESV and LVEF compared with QGS [20] The CAFU method has also been integrated in a computer based decision support system, which is aiding physicians in the diagnosis of myocardial infarction and ischaemia in MPS studies. Compared with expert interpretation the decision support system has a sensitivity of 89% and a specificity of 96% for the diagnosis of myocardial infarct; a sensitivity of 90% and a specificity of 85% for the diagnosis of ischaemia [21].…”