A new computerized acute coronary syndrome (ACS) computer algorithm has been developed with the aim of improving the electrocardiographic detection of acute myocardial ischemia and infarction in the emergency department (ED). The purpose of this study was to determine the added value of the new ACS algorithm in assisting ED physicians to obtain a more accurate diagnosis in patients with ACS. The new algorithm combines a rule-based decision tree, which uses well-known clinical criteria and a data-centered neural network model for more robust pattern recognition. Input parameters of the neural network model consist of morphology features of derived Frank X, Y, Z waveforms and the patient's gender and age. The neural network model was trained with electrocardiograms obtained from documented acute myocardial infarction patients at the Mayo Clinic who were a part of a research ACS database, which includes electrocardiograms (ECGs) of more than 5,000 individuals at hospital admission (1st ECG in the ED). The test set portion of the study was conducted in 2 steps: 1) One emergency physician and 1 cardiologist classified 1,902 clinically correlated out-of-hospital ECGs without seeing the interpretation statement from the algorithm into 1 of the following categories: 1) acute myocardial infarction, acute ischemia, or nonischemic; 2) After 9 months, the same 2 physicians classified the same group of ECGs but with the interpretation statement of the algorithm printed on the tracing. The results demonstrated that with the assistance of the new algorithm, the emergency physician and cardiologist improved their sensitivity of interpreting acute myocardial infarction by 50% and 26%, respectively, without a loss of specificity. The new algorithm also improved the emergency physician's acute ischemia interpretation sensitivity by 53% and still maintained a reasonable specificity (91%). The new ACS algorithm provides added value for improving acute ischemia and infarction detection in the ED.