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OTITIS MEDIA VOCABULARY AND GRAMMAR.
Kuruvilla, Anupama; Li, Jian; Yeomans, Pablo Hennings; Quelhas, Pedro; Shaikh, Nader; Hoberman, Alejandro; Kovacevic, Jelena.
Afiliación
  • Kuruvilla A; Dept. of BME and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Li J; Dept. of ECE, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Yeomans PH; Dept. of BME and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA.
  • Quelhas P; Universidade do Porto, Faculdade de Engenharia (DEEC), INEB - Instituto de Engenharia Biomedica, Porto, Portugal.
  • Shaikh N; Division of General Academic Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Hoberman A; Division of General Academic Pediatrics, Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Kovacevic J; Dept. of BME and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA, USA ; Dept. of ECE, Carnegie Mellon University, Pittsburgh, PA, USA.
Proc Int Conf Image Proc ; 2012: 2845-2848, 2012.
Article en En | MEDLINE | ID: mdl-25018671
ABSTRACT
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc Int Conf Image Proc Año: 2012 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Proc Int Conf Image Proc Año: 2012 Tipo del documento: Article País de afiliación: Estados Unidos