HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Thomas, B. J. Articles by Rosenthal, D. I. Search for Related Content PubMed PubMed Citation Articles by Thomas, B. J. Articles by Rosenthal, D. I.

Automated Computer-Assisted Categorization of Radiology Reports

¹ All authors: Division of Musculoskeletal Radiology, Department of Radiology, Massachusetts General Hospital, 32 Fruit St., YAW 6E, Boston, MA 02114.

OBJECTIVE. The objective of our study was to create and validate an automated computerized method for the categorization of narrative text radiograph reports.

MATERIALS AND METHODS. Using commercially available software with embedded Boolean logic, we created a text search algorithm to categorize reports of radiography examinations into "fracture," "normal," and "neither normal nor fracture." The algorithm was refined and optimized through repeated testing on 512 consecutive ankle radiography reports from a single clinical imaging center. The final algorithm was applied on a different set of 750 consecutive radiography reports of the spine and extremities produced at three different clinical imaging sites and interpreted by 44 different radiologists. Expert reviewers assessed the accuracy of the final classification. The chi-square test or Fisher's exact test was performed to determine the reproducibility of results across different clinical imaging sites.

RESULTS. The computerized classification was highly accurate for the classification of radiography reports into "normal" (specificity, 91.6%; sensitivity, 91.3%), "neither normal nor fracture" (sensitivity, 87.8%; specificity, 94.9%), and "fracture" (sensitivity, 94.1%; specificity, 98.1%) categories. This performance showed no significant difference across the three sites (p > 0.05).

CONCLUSION. Computerized categorization of narrative-text radiography reports is highly sensitive and specific and can be used to classify reports from different imaging sites generated by different radiologists. This method can be an extremely powerful tool in future cost-effectiveness studies, health care policy studies, operations assessments, and quality control.

This article has been cited by other articles:

				M. Torriani, B. J. Thomas, M. A. Bredella, and H. Ouellette MRI of Metatarsal Head Subchondral Fractures in Patients with Forefoot Pain Am. J. Roentgenol., March 1, 2008; 190(3): 570 - 575. [Abstract] [Full Text] [PDF]

				B. de Bruijn, A. Cranney, S. O'Donnell, J. D. Martin, and A. J. Forster Identifying Wrist Fracture Patients with High Accuracy by Automatic Categorization of X-ray Reports J. Am. Med. Inform. Assoc., November 1, 2006; 13(6): 696 - 698. [Abstract] [Full Text] [PDF]

				J. H. Thrall Reinventing Radiology in the Digital Age: Part II. New Directions and New Stakeholder Value Radiology, October 1, 2005; 237(1): 15 - 18. [Full Text] [PDF]