Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis

• Published in: EBioMedicine 2016-03, Vol.5 (C), p.156-160
• Main Authors: Myburgh, Hermanus C., van Zijl, Willemien H., Swanepoel, DeWet, Hellström, Sten, Laurent, Claude
• Format: Article
• Language: English
• Subjects: Automated diagnosis; Decision tree; Developing Countries; Feature extraction; Global medicine; Humans; Image Processing, Computer-Assisted - instrumentation; Image Processing, Computer-Assisted - methods; Otitis media; Otitis Media - diagnosis; Otitis Media - diagnostic imaging; Otitis Media - physiopathology; Research Paper; Tympanic Membrane - diagnostic imaging; Tympanic Membrane - physiopathology; Video-otoscope
• ISSN: 2352-3964; 2352-3964
• DOI: 10.1016/j.ebiom.2016.02.017

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. 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. 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. 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. •Computer-based otitis media classification can provide access to diagnosis in developing countries.•Diagnostic accuracy of the image-analysis classification system was comparable to general practitioners' and pediatricians'. Most people globally do not have access to health care specialists who can diagnose one of the most common childhood illnesses, otitis media (middle ear infection). Affecting more than half a billion people annually, early and accurate diagnosis can ensure appropriate treatment to minimize the widespread impact of ear infections. We developed an image-analysis classification system that can diagnose otitis media with an accuracy comparable to that of general practitioners and pediatricians. The software system, which can be cloud-based for remote image uploading, could provide rapid access to accurate diagnoses in developing countries.