title: Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs.
abstract: OBJECTIVE
  We developed a new method to distinguish between various interstitial lung diseases that uses an artificial neural network. This network is based on features extracted from chest radiographs and clinical parameters. The aim of our study was to evaluate the effect of the output from the artificial neural network on radiologists' diagnostic accuracy.
  
  
  METHODS
  The artificial neural network was designed to differentiate among 11 interstitial lung diseases using 10 clinical parameters and 16 radiologic findings. Thirty-three clinical cases (three cases for each lung disease) were selected. In the observer test, chest radiographs were viewed by eight radiologists (four attending physicians and four residents) with and without network output, which indicated the likelihood of each of the 11 possible diagnoses in each case. The radiologists' performance in distinguishing among the 11 interstitial lung diseases was evaluated by receiver operating characteristic (ROC) analysis with a continuous rating scale.
  
  
  RESULTS
  When chest radiographs were viewed in conjunction with network output, a statistically significant improvement in diagnostic accuracy was achieved (p < .0001). The average area under the ROC curve was .826 without network output and .911 with network output.
  
  
  CONCLUSIONS
  An artificial neural network can provide a useful "second opinion" to assist radiologists in the differential diagnosis of interstitial lung disease using chest radiographs.
authors.affiliation: Department of Radiology, The University of Chicago, IL 60637, USA.
authors.forename: K
authors.initials: K
authors.lastname: Ashizawa
authors.forename: H
authors.initials: H
authors.lastname: MacMahon
authors.forename: T
authors.initials: T
authors.lastname: Ishida
authors.forename: K
authors.initials: K
authors.lastname: Nakamura
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: D003937:Diagnosis, Differential; D006801:Humans; D017563:Lung Diseases, Interstitial / Q000000981:diagnostic imaging* / Q000453:epidemiology; D016571:Neural Networks, Computer; D015588:Observer Variation; D012372:ROC Curve; D013902:Radiography, Thoracic / Q000706:statistics & numerical data