Ashizawa K, Ishida T, MacMahon H, Vyborny C J, Katsuragawa S, Doi K
Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA.
Acad Radiol. 1999 Jan;6(1):2-9. doi: 10.1016/s1076-6332(99)80055-5.
The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease.
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.
The Az (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 correct diagnosis with the two largest output values among the 11 diseases.
ANNs using clinical parameters and radiologic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.
作者评估了人工神经网络（ANNs）在间质性肺疾病鉴别诊断中的作用。
作者使用了具有反向传播算法的三层前馈人工神经网络。这些人工神经网络旨在根据胸部放射科医生提取的10个临床参数和16项放射学表现，区分11种间质性肺疾病。因此，人工神经网络由26个输入单元和11个输出单元组成。通过循环（或留一法）技术，使用150例实际临床病例、先前发表文章中的110例病例以及110例假设病例对人工神经网络进行训练和测试。通过受试者操作特征（ROC）分析评估人工神经网络的性能。
实际临床病例获得的Az（ROC曲线下面积）为0.947，在根据11种疾病中两个最大输出值指示正确诊断方面，人工神经网络的敏感性和特异性均约为90%。
使用临床参数和放射学表现的人工神经网络可能有助于在胸部X光片上进行间质性肺疾病的鉴别诊断。
