Artificial neural networks in chest radiography: Application to the differential diagnosis of interstitial lung disease

Rationale and Objectives. The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease. Materials and Methods. The authors used three-layer,;feed-forward ANNs with a back-propagation algorithm. The ANNs were designed to distinguish between 11 interstitial lung diseases on the basis of 10 clinical parameters and 16 radiologic findings extracted by chest radiologists. Thus, the ANNs consisted of 26 input units and 11 output units. One hundred fifty actual clinical cases, 110 cases from previously published articles, and 110 hypothetical cases were used for training and testing the ANNs by using a round-robin (or leave-one-out) technique. ANN performance was evaluated with receiver operating characteristic (ROC) analysis. Results. The AI (area under the ROC curve) obtained with actual clinical cases was 0.947, and both the sensitivity and specificity of the ANNs were approximately 90% in terms of indicating the comet diagnosis with the two largest output values among the 11 diseases. Conclusion. ANNs using clinical parameters and radio-logic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.