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Improved Classifications of Myocardial Bull's-Eye Scintigrams with Computer-Based Decision Support System

Departments of Clinical Physiology, Statistics, Radiology, and Radiation Physics, Lund University, Lund, Sweden

Correspondence: For correspondence or reprints contact: Dan Lindahl, MSc, Department of Clinical Physiology, University Hospital, S-221 85 Lund, Sweden.

ABSTRACT

In a recent study, artificial neural networks were trained to detect coronary artery disease using scintigraphic data as input. The performance of the networks was better than that of human experts using coronary angiography as a gold standard. In clinical practice, this type of neural networks will not take over the decision-making process from the physician but will assist by proposing an interpretation of the scintigram. The purpose of this study was to assess the influence of such decision support on the interpretations of the physicians. Methods: A population of 135 patients who had undergone both myocardial ^99mTc-sestamibi rest/stress scintigraphy and coronary angiography within a 3-mo period was studied. An image set consisting of the bull's-eye rest, stress, difference and quote images was constructed for each patient. Three experienced physicians independently classified all image sets regarding the presence and/or absence of coronary artery disease in two vascular territories using a four-grade scale. The physicians classified the image sets twice with and twice without the advice of artificial neural networks. Results: The joint evaluation of the three physicians showed significantly improved performance with decision support, measured as increases in the areas under the receiver operating characteristic curves from 0.65 to 0.70 (P = 0.018) and from 0.79 to 0.82 (P = 0.006) for two vascular territories. Furthermore, the joint evaluation showed significantly less intraobserver and interobserver variability with decision support. Conclusion: Physicians classifying myocardial bull's-eye images benefit from the advice of artificial neural networks. These results show the high potential for neural networks as clinical decision support systems.

Key Words: artificial intelligence • computer-assisted diagnosis • ischemic heart disease • radionuclide imaging

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