Automated interpretation of myocardial SPECT perfusion images using artificial neural networks

The purpose of this study was to develop a computer-based method for automatic detection and localization of coronary artery disease (CAD) in myocardial bull's-eye scintigrams.

A population of 135 patients who had undergone both myocardial 99mTc-sestamibi rest-stress scintigraphy and coronary angiography within 3 mo was studied. Different image data reduction methods, including pixel averaging and two-dimensional Fourier transform, were applied to the bull's-eye scintigrams. After a quantitative and qualitative evaluation of these methods, 30 Fourier components were chosen as inputs to multilayer perceptron artificial neural networks. The networks were trained to detect CAD in two vascular territories, using coronary angiography as gold standard. A "leave one out" procedure was used for training and evaluation. The performance of the networks was compared to those of two human experts.

One of the human experts detected CAD in one of two vascular territories, with a sensitivity of 54.4% at a specificity of 70.5%. The sensitivity of the networks was significantly higher at that level of specificity (77.2%, p = 0.0022). The other expert had a sensitivity of 63.2% at a specificity of 61.5%. The networks had a sensitivity of 77.2% (p = 0.038) at this specificity level as well. The differences in sensitivity between human experts and networks for the other vascular territory were all less than 6% and were not statistically significant.

Artificial neural networks can detect CAD in myocardial bull's-eye scintigrams with such a high accuracy that the application of neural networks as clinical decision support tools appears to have significant potential.