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