Myocardial SPET: artificial neural networks describe extent and severity of perfusion defects

Abstract

Artificial neural networks are computer programs that learn from examples. They have been successfully used to detect coronary artery disease from myocardial perfusion images. The purpose of the present study was to develop neural networks that could classify myocardial scintigrams regarding reversibility, localization, severity and extent of perfusion defects. Rest/exercise technetium-99m sestamibi scintigrams from 338 patients were studied. The classifications of two experts were employed as the gold standard. Artificial neural networks were trained to classify both reversible (ischaemia) and non-reversible (infarct) defects in three vascular territories, corresponding to the main coronary arteries. The extent (small or large) and severity (mild or severe) of the defects were described by the networks. After the training process, separate test sets were used to compare the neural networks with one of the experts who reclassified the scintigrams two months later. The neural networks made correct classifications in 71% of the test cases and the human expert in 70% (P=0.10). It was concluded that artificial neural networks can be trained to make clinical interpretations of myocardial perfusion scintigrams. The results indicate that networks can assist physicians in achieving correct interpretations and thereby improve the diagnostic accuracy of medical imaging.