title: Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis.
abstract: BACKGROUND
  Otitis media is one of the most common childhood diseases worldwide, but because of lack of doctors and health personnel in developing countries it is often misdiagnosed or not diagnosed at all. This may lead to serious, and life-threatening complications. There is, thus a need for an automated computer based image-analyzing system that could assist in making accurate otitis media diagnoses anywhere.
  
  
  METHODS
  A method for automated diagnosis of otitis media is proposed. The method uses image-processing techniques to classify otitis media. The system is trained using high quality pre-assessed images of tympanic membranes, captured by digital video-otoscopes, and classifies undiagnosed images into five otitis media categories based on predefined signs. Several verification tests analyzed the classification capability of the method.
  
  
  RESULTS
  An accuracy of 80.6% was achieved for images taken with commercial video-otoscopes, while an accuracy of 78.7% was achieved for images captured on-site with a low cost custom-made video-otoscope.
  
  
  CONCLUSIONS
  The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (~64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations.
authors.affiliation: Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa.
authors.forename: Hermanus C
authors.initials: HC
authors.lastname: Myburgh
authors.affiliation: Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria, South Africa.
authors.forename: Willemien H
authors.initials: WH
authors.lastname: van Zijl
authors.affiliation: Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa; Ear Sciences Centre, School of Surgery, The University of Western Australia, Nedlands, Australia; Ear Science Institute Australia, Subiaco, Western Australia.
authors.forename: DeWet
authors.initials: D
authors.lastname: Swanepoel
authors.affiliation: Department of Audiology and Neurotology, Karolinska University Hospital, Stockholm, Sweden; CLINTEC/Otorhinolaryngology, Karolinska Institutet, Stockholm, Sweden.
authors.forename: Sten
authors.initials: S
authors.lastname: Hellström
authors.affiliation: Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa; Department of Clinical Science, University of Umeå, Umeå, Sweden.
authors.forename: Claude
authors.initials: C
authors.lastname: Laurent
mesh_terms: D003906:Developing Countries; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D010033:Otitis Media / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000503:physiopathology; D014432:Tympanic Membrane / Q000000981:diagnostic imaging* / Q000503:physiopathology
keywords: Automated diagnosis; Decision tree; Feature extraction; Global medicine; Otitis media; Video-otoscope