Single-photon emission computed tomographic imaging is a useful, noninvasive method to detect coronary artery disease (CAD). We tested the hypothesis that artificial neural network modeling could predict CAD extent better than visual interpretation; 109 patients who underwent stress single-photon emission computed tomography and coronary angiography were selected. Twenty patients who had a <5% probability of CAD were also selected for calculation of normalcy rate. A model was trained for each vessel. Stress images were decreased to 25 points by pixel averaging the polar map. The model output was 1 for vessel stenosis >60% and 0 otherwise. Model sensitivities were 92% (55 of 60) for left anterior descending artery versus 62% (37 of 60) for visual interpretation (p = 0.0002), 69% (20 of 29) for left circumflex artery versus 55% for visual interpretation (p = 0.30), and 94% (45 of 48) for right coronary artery versus 78% for visual interpretation (p = 0.024). Model specificities and normalcy rates were 78% and 85% for the left anterior descending artery, 93% and 100% for the left circumflex artery, and 85% and 90% for the right circumflex artery, respectively. Single-vessel CAD was predicted in 27 of 28 patients (96%) by modeling versus 23 of 28 patients (82%) by visual interpretation (p = 0.11). Multivessel CAD was correctly predicted in 30 of 46 patients (65%) by modeling versus 16 of 46 patients (35%) by visual interpretation (p = 0.004). Thus, artificial neural network models can predict CAD from stress single-photon emission computed tomographic images when using separate models for the 3 major epicardial vessels. Because of their high sensitivity and specificity in detecting extensive CAD, these models have great promise as an aid to correctly identify patients at high risk for CAD.