Accuracy of an artificial neural network for detecting a regional abnormality in myocardial perfusion SPECT

The patient-based diagnosis with an artificial neural network (ANN) has shown potential utility for the detection of coronary artery disease; however, the region-based accuracy of the detected regions has not been fully evaluated. The aim of this study was to demonstrate the accuracy of all detected regions compared with expert interpretation.

A total of 109 abnormal regions including 33 regions with stress defects and 76 regions with ischemia were examined, which were derived from 21 patients who underwent myocardial perfusion SPECT within 45 days of coronary angiography. The gray and color scale images, a polar map of stress, rest and difference, and left ventricular function were displayed on the monitor to score the extent and severity of stress defect and ischemia. Two experienced nuclear medicine physicians (Observers A and B) scored the abnormality with a 4-point scale and draw abnormal regions on a polar map. The gold standard was determined by the final judgment of normal or abnormal by the consensus of two other independent expert nuclear cardiologists, and was compared with the stress defect and ischemia derived from ANN.

The concordance rate of ANN to the gold standard was higher than that of two observers. Furthermore, the κ coefficient indicated moderate to substantial agreement for stress defect and slight to the fair agreement for ischemia. The area under the curve (AUC) of ANN was the highest for both stress defect and ischemia; in particular, the ANN of ischemia showed significantly higher AUC than Observer A (p = 0.005). The ANN of stress defect showed higher specificity compared with two observers, while the ANN of ischemia showed higher sensitivity. Consequently, the accuracy of ANN showed the highest in this study.

The ANN-based regional diagnosis showed a high concordance rate with the gold standard and comparable or even higher than the interpretation by nuclear medicine physicians.