Deep Learning to Classify Radiology Free-Text Reports.

Journal Article (Journal Article;Multicenter Study)

Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

Full Text

Duke Authors

Cited Authors

  • Chen, MC; Ball, RL; Yang, L; Moradzadeh, N; Chapman, BE; Larson, DB; Langlotz, CP; Amrhein, TJ; Lungren, MP

Published Date

  • March 2018

Published In

Volume / Issue

  • 286 / 3

Start / End Page

  • 845 - 852

PubMed ID

  • 29135365

Electronic International Standard Serial Number (EISSN)

  • 1527-1315

Digital Object Identifier (DOI)

  • 10.1148/radiol.2017171115


  • eng

Conference Location

  • United States