title: Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease.
abstract: OBJECTIVE
  The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease.
  
  
  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 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.
  
  
  CONCLUSIONS
  ANNs using clinical parameters and radiologic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA.
authors.forename: K
authors.initials: K
authors.lastname: Ashizawa
authors.forename: T
authors.initials: T
authors.lastname: Ishida
authors.forename: H
authors.initials: H
authors.lastname: MacMahon
authors.forename: C J
authors.initials: CJ
authors.lastname: Vyborny
authors.forename: S
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: K
authors.initials: K
authors.lastname: Doi
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D019540:Area Under Curve; D002648:Child; D019992:Databases as Topic; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000145:classification / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D013902:Radiography, Thoracic; D012680:Sensitivity and Specificity