A Neural Network That Learns to Interpret Myocardial Planar Thallium Scintigrams

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Abstract
The planar thallium-201 (201Tl) myocardial perfusion scintigram is a widely used diagnostic technique for detecting and estimating the risk of coronary artery disease. Interpretation is currently based on visual scoring of myocardial defects combined with image quantitation and is known to have a significant subjective component. Neural networks learned to interpret thallium scintigrams as determined by both individual and multiple (consensus) expert ratings. Four different types of networks were explored: single-layer, two-layer backpropagation (BP), BP with weight smoothing, and two-layer radial basis function (RBF). The RBF network was found to yield the best performance (94.8% generalization by region) and compares favorably with human experts. We conclude that this network is a valuable clinical tool that can be used as a reference "diagnostic support system" to help reduce inter- and intraobserver variability. This system is now being further developed to include other variables that are expected to improve the final clinical diagnosis.