The principle objective of the proposed research is to determine whether the diagnosis of regional dysfunction indicative of myocardial infarction, and the assessment of prognosis can be more accurately performed from analysis of regional left ventricular curvature than from analysis of left ventricular wall motion. Data accumulated during the NIH sponsored trial of Thrombolysis In Myocardial Infarction (TIMI) will be analyzed. An advanced computational technique will be used to correlate curvature measurements with diagnosis and with prognosis: the artificial neural network, which applies the experience gained from processing patient data to form associations between the input data (ventricular curvature) and the desired output (infarct diagnosis, prognosis). The accuracy with which the neural network assisted evaluation of regional left ventricular curvature can distinguish infarct (TIMI) patients from patients with normal coronary arteries will be compared with the diagnostic accuracy of traditional wall motion analysis and of neural network assisted analysis of wall motion. In addition, the ability of the neural network to predict survival from analysis of function will be compared with the accuracy of predicting survival using Cox regression analysis. The results of the study will help to evaluate patients with angina and nondiagnostic electrocardiograms, by improving the accuracy with which regional dysfunction can be identified. The results of the study may also help identify patients at high risk who may benefit from more aggressive therapy. It is envisioned that artificial neural network assisted analysis of regional ventricular curvature will ultimately be applied to two-dimensional echocardiography, to which curvature analysis is theoretically more suitable than wall motion, thus enabling noninvasive diagnosis and prognostication.