Artificial neural networks (ANNs) for differential diagnosis of interstitial lung disease: results of a simulation test with actual clinical cases.
OBJECTIVE
To evaluate the performance of an artificial neural network (ANN) scheme with use of consecutive clinical cases and its effect on radiologists with an observer test.


METHODS
Artificial neural networks were designed to distinguish among 11 interstitial lung diseases on the basis of 26 inputs (16 radiologic findings, 10 clinical parameters). Chest radiographs of 96 consecutive cases with interstitial lung disease were used. Five radiologists independently rated their radiologic findings on the 96 chest radiographs. Based on their ratings of radiologic findings and clinical parameters obtained from the hospital information system, the output values indicating the likelihood of each of the 11 interstitial lung diseases were determined. Subsequently, 30 cases were selected from these 96 cases for an observer test. Five radiologists marked their confidence levels for diagnosis of 11 possible diseases in each case without and with ANN output. The performance of ANNs and radiologists was evaluated by receiver operating characteristic analysis based on their outputs and on confidence levels, respectively. RESULTS; The average Az value (area under the receiver operating characteristic curve) indicating ANN performance for the 96 consecutive cases was 0.85 +/- 0.03. The average Az values indicating radiologists' performance without and with ANN outputs were 0.81 +/- 0.11 and 0.87 +/- 0.06, respectively. The diagnostic accuracy was improved significantly when radiologists read chest radiographs with ANN outputs (P < .05).


CONCLUSIONS
Artificial neural networks for differential diagnosis of interstitial lung disease may be useful in clinical situations, and radiologists may be able to utilize the ANN output to their advantage in the differential diagnosis of interstitial lung disease on chest radiographs.
Abe
Hiroyuki
H
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, The University of Chicago, MC 2026, 5841 S Maryland Ave, Chicago, IL 60637, USA.
Ashizawa
Kazuto
K
Li
Feng
F
Matsuyama
Naohiro
N
Fukushima
Aya
A
Shiraishi
Junji
J
MacMahon
Heber
H
Doi
Kunio
K
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002983:Clinical Competence; D003198:Computer Simulation; D003937:Diagnosis, Differential; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D012372:ROC Curve; D015642:Radiology, Interventional