Automated diagnosis of otitis media: vocabulary and grammar

  • Authors:

  • Anupama Kuruvilla;Nader Shaikh;Alejandro Hoberman;Jelena Kovačević

  • Affiliations:

  • Department of BME and Center for Bioimage Informatics, Carnegie Mellon University, Pittsburgh, PA;Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA;Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA;Department of BME and Center for Bioimage Informatics and Department of ECE Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Journal of Biomedical Imaging - Special issue on Computer Vision and Image Processing for Computer-Aided Diagnosis
  • Year:
  • 2013

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Abstract

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.