Otitis Media Diagnosis for Developing Countries Using Tympanic Membrane Image-Analysis
2016 (English)In: EBioMedicine, ISSN 0360-0637, E-ISSN 2352-3964, Vol. 5, 156-160 p.Article in journal (Refereed) PublishedText
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. Findings: 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. Interpretation: The high accuracy of the proposed otitis media classification system compares well with the classification accuracy of general practitioners and pediatricians (similar to 64% to 80%) using traditional otoscopes, and therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved populations. (C) 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Place, publisher, year, edition, pages
2016. Vol. 5, 156-160 p.
Decision tree, Feature extraction, Automated diagnosis, Otitis media, Video-otoscope, Global medicine
IdentifiersURN: urn:nbn:se:umu:diva-121597DOI: 10.1016/j.ebiom.2016.02.017ISI: 000375078200029PubMedID: 27077122OAI: oai:DiVA.org:umu-121597DiVA: diva2:940733