Prospective validation of artificial neural network trained to identify acute myocardial infarction.
BACKGROUND
Artificial neural networks apply non-linear statistics to pattern recognition problems. One such problem is acute myocardial infarction (AMI), a diagnosis which, in a patient presenting as an emergency, can be difficult to confirm. We report here a prospective comparison of the diagnostic accuracy of a network and that of physicians, on the same patients with suspected AMI.


METHODS
Emergency department physicians who evaluated 1070 patients 18 years or older presenting to the emergency department of a teaching hospital in California, USA with anterior chest pain indicated whether they thought these patients had sustained a myocardial infarction. The network analysed the patient data collected by the physicians during their evaluations and also generated a diagnosis.


RESULTS
The physicians had a diagnostic sensitivity and specificity for myocardial infarction of 73.3% (95% confidence interval 63.3-83.3%) and 81.1% (78.7-83.5%), respectively, while the network had a diagnostic sensitivity and specificity of 96.0% (91.2-100%) and 96.0% (94.8-97.2%), respectively. Only 7% of patients had had an AMI, a low frequency but typical for anterior chest pain.


CONCLUSIONS
The application of non-linear neural computational analysis via an artificial neural network to the clinical diagnosis of myocardial infarction appears to have significant potential.
Department of Emergency Medicine, University of California, San Diego Medical Center, USA.
W G
WG
Baxt
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Skora
D002637:Chest Pain / Q000175:diagnosis; D004562:Electrocardiography; D004635:Emergency Medicine; D004636:Emergency Service, Hospital; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis*; D016571:Neural Networks, Computer; D010820:Physicians; D011446:Prospective Studies; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity