Potential usefulness of an artificial neural network for differential diagnosis of interstitial lung diseases: pilot study.
An artificial neural network approach was applied to the differential diagnosis of interstitial lung diseases. The neural network was designed to distinguish between nine types of interstitial lung diseases on the basis of 20 items of clinical and radiographic information. A data base for training and testing the neural network was created with 10 hypothetical cases for each of the nine diseases. The performance of the neural network was evaluated by means of receiver operating characteristic analysis. The decision performance of the neural network was high; it was comparable to that of chest radiologists and superior to that of senior radiology residents. The preliminary results strongly suggest that the neural network approach has potential utility in the computer-aided differential diagnosis of interstitial lung diseases.
Asada
N
N
Department of Radiology, University of Chicago, IL 60637.
Doi
K
K
MacMahon
H
H
Montner
S M
SM
Giger
M L
ML
Abe
C
C
Wu
Y
Y
D003198:Computer Simulation; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006801:Humans; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008959:Models, Neurological; D010363:Pattern Recognition, Automated; D010865:Pilot Projects; D012372:ROC Curve; D011859:Radiography