Value of exercise data for the interpretation of myocardial perfusion SPECT.
BACKGROUND
Artificial neural networks have successfully been applied for automated interpretation of myocardial perfusion images. So far the networks have used data from the myocardial perfusion images only. The purpose of this study was to investigate whether the automated interpretation of myocardial perfusion images with the use of artificial neural networks was improved if clinical data were assessed in addition to the perfusion images.


RESULTS
A population of 229 patients who had undergone both rest-stress myocardial perfusion scintigraphy in conjunction with an exercise test and coronary angiography, with no more than 3 months elapsing between the 2 examinations, were studied. The networks were trained to detect coronary artery disease or myocardial ischemia with the use of 2 different gold standards. The first was based on coronary angiography, and the second was based on all data available (including perfusion scintigrams, coronary angiography, exercise test, resting electrocardiography, patient history, etc). The performance of the neural networks was quantified as areas under the receiver operating characteristic curves. The results showed that the neural networks trained with perfusion images performed better than those trained with exercise data (0.78 vs 0.55, P <.0001), with coronary angiography used as the gold standard. Furthermore, the networks did not improve when data from the exercise test were used as input in addition to the perfusion images (0.78 vs 0.77, P =.6).


CONCLUSIONS
The results show that the clinically important information in combined exercise test and myocardial scintigraphy could be found in the perfusion images. Exercise test information did not improve upon the accuracy of automated neural network interpretation of myocardial perfusion images in a receiver operator characteristic analysis of test accuracy.
Haraldsson
Henrik
H
Complex Systems Division, Department of Theoretical Physics, University of Lund, Sweden.
Ohlsson
Mattias
M
Edenbrandt
Lars
L
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D017023:Coronary Angiography; D003327:Coronary Disease / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography; D005080:Exercise Test; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods