TY - JOUR
T1 - Automated diagnosis of otitis media
T2 - Vocabulary and grammar
AU - Kuruvilla, Anupama
AU - Shaikh, Nader
AU - Hoberman, Alejandro
AU - Kovačević, Jelena
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
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U2 - 10.1155/2013/327515
DO - 10.1155/2013/327515
M3 - Article
C2 - 23997759
AN - SCOPUS:84883172994
SN - 1687-4188
VL - 2013
JO - International Journal of Biomedical Imaging
JF - International Journal of Biomedical Imaging
M1 - 327515
ER -