TY - GEN
T1 - Otitis media vocabulary and grammar
AU - Kuruvilla, Anupama
AU - Li, Jian
AU - Yeomans, Pablo Hennings
AU - Quelhas, Pedro
AU - Shaikh, Nader
AU - Hoberman, Alejandro
AU - Kovaĉević, Jelena
PY - 2012
Y1 - 2012
N2 - We propose 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 and otitis media with effusion a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is hard, leading to overprescription of antibiotics that are beneficial only for children with acute otitis media, prompting a need for an accurate and automated 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 otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this otitis media grammar. The algorithm achieves 84% classification accuracy, in the range or outperforming clinicians who did not receive special training, as well as state-of-the-art classifiers.
AB - We propose 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 and otitis media with effusion a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is hard, leading to overprescription of antibiotics that are beneficial only for children with acute otitis media, prompting a need for an accurate and automated 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 otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this otitis media grammar. The algorithm achieves 84% classification accuracy, in the range or outperforming clinicians who did not receive special training, as well as state-of-the-art classifiers.
KW - classification
KW - grammar
KW - otitis media
KW - vocabulary
UR - http://www.scopus.com/inward/record.url?scp=84875822878&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875822878&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2012.6467492
DO - 10.1109/ICIP.2012.6467492
M3 - Conference contribution
AN - SCOPUS:84875822878
SN - 9781467325332
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 2845
EP - 2848
BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
T2 - 2012 19th IEEE International Conference on Image Processing, ICIP 2012
Y2 - 30 September 2012 through 3 October 2012
ER -