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International Journal of Biomedical Imaging
Volume 2013, Article ID 327515, 15 pages
http://dx.doi.org/10.1155/2013/327515
Research Article

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

1Department of BME and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2Division of General Academic Pediatrics, Children’s Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
3Department of ECE Carnegie Mellon University, Pittsburgh, PA 15213, USA

Received 5 April 2013; Revised 15 June 2013; Accepted 6 July 2013

Academic Editor: Fabrice Meriaudeau

Copyright © 2013 Anupama Kuruvilla et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We propose a novel automated algorithm for classifying diagnostic categories of otitis media: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with effusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult, often leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the need for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The algorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers.