This thesis presents an automated algorithm for classifying diagnostic categories of otitis media (middle ear inflammation): 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 as it is often confused with otitis media with effusion leading to overprescription of antimicrobials as they are beneficial only for children with acute otitis media. Such misdiagnoses is of increasing concern as it leads to mismanaged episodes of otitis media and most importantly compromises the efficacy of any future treatments for a bacterial infection. The current standard of clinical diagnosis of otitis media is visual examination of the tympanic membrane, this manual and subjective evaluation has clearly shown its limitations prompting 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 93.5% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers.
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