paper information and status

A. Kuruvilla, N. Shaikh, A. Hoberman, and J. Kovačević. Automated diagnosis of otitis media: A vocabulary and grammar. Int. J. Biomed. Imag., sp. iss. Computer Vis. Image Process. for Computer-Aided Diagnosis, Aug. 2013.

[ pdf | bibtex]


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.



The zipped archive contains the readme file as well as the code to generate the results in the paper.


This work is licensed under a Creative Commons GNU General Public License. To view a copy of this license, visit If you use this code or any part thereof in your research or publication, please also include a reference to this paper. Thank you!

list of tested configurations

MATLAB Version (R2011a) on Windows 7 Home Premium.


For more information or to report bugs contact jelenak at cmu dot edu.