W G Baxt1. 1. University of California, San Diego Medical Center.
Abstract
OBJECTIVE: To validate prospectively the use of an artificial neural network to identify myocardial infarction in patients presenting to an emergency department with anterior chest pain. DESIGN: Prospective, blinded testing. SETTING: Tertiary university teaching center. PATIENTS: A total of 331 consecutive adult patients presenting with anterior chest pain. MEASUREMENTS: Diagnostic sensitivity and specificity with regard to the diagnosis of acute myocardial infarction. MAIN RESULTS: An artificial neural network was trained on clinical pattern sets retrospectively derived from the cases of 351 patients hospitalized with a high likelihood of having myocardial infarction. It was prospectively tested on 331 consecutive patients presenting to an emergency department with anterior chest pain. The ability of the network to distinguish patients with from those without acute myocardial infarction was compared with that of physicians caring for the same patients. The physicians had a diagnostic sensitivity of 77.7% (95% CI, 77.0% to 82.9%) and a diagnostic specificity of 84.7% (CI, 84.0% to 86.4%). The artificial neural network had a sensitivity of 97.2% (CI, 97.2% to 97.5%; P = 0.033) and a specificity of 96.2% (CI, 96.2% to 96.4%; P less than 0.001). CONCLUSION: An artificial neural network trained to identify myocardial infarction in adult patients presenting to an emergency department may be a valuable aid to the clinical diagnosis of myocardial infarction; however, this possibility must be confirmed through prospective testing on a larger patient sample.
OBJECTIVE: To validate prospectively the use of an artificial neural network to identify myocardial infarction in patients presenting to an emergency department with anterior chest pain. DESIGN: Prospective, blinded testing. SETTING: Tertiary university teaching center. PATIENTS: A total of 331 consecutive adult patients presenting with anterior chest pain. MEASUREMENTS: Diagnostic sensitivity and specificity with regard to the diagnosis of acute myocardial infarction. MAIN RESULTS: An artificial neural network was trained on clinical pattern sets retrospectively derived from the cases of 351 patients hospitalized with a high likelihood of having myocardial infarction. It was prospectively tested on 331 consecutive patients presenting to an emergency department with anterior chest pain. The ability of the network to distinguish patients with from those without acute myocardial infarction was compared with that of physicians caring for the same patients. The physicians had a diagnostic sensitivity of 77.7% (95% CI, 77.0% to 82.9%) and a diagnostic specificity of 84.7% (CI, 84.0% to 86.4%). The artificial neural network had a sensitivity of 97.2% (CI, 97.2% to 97.5%; P = 0.033) and a specificity of 96.2% (CI, 96.2% to 96.4%; P less than 0.001). CONCLUSION: An artificial neural network trained to identify myocardial infarction in adult patients presenting to an emergency department may be a valuable aid to the clinical diagnosis of myocardial infarction; however, this possibility must be confirmed through prospective testing on a larger patient sample.
Authors: Toyoko S Yamashita; Isaac F Nuamah; Philip A Dorsey; Seyed M Hosseini-Nezhad; Roger A Bielefeld; Edward F Kerekes; Lynn T Singer Journal: Comput Med Public Health Biotechnol (1994) Date: 1995
Authors: Marco V Perez; Frederick E Dewey; Swee Y Tan; Jonathan Myers; Victor F Froelicher Journal: Ann Noninvasive Electrocardiol Date: 2009-01 Impact factor: 1.468