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 imageanalyzing
system that could assist in making accurate otitis media diagnoses anywhere.
METHODS : A method for automated diagnosis of otitismedia 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 (~64% to 80%) using traditional otoscopes, and
therefore holds promise for the future in making automated diagnosis of otitis media in medically underserved
populations.
