The new method helps health personnel to diagnose middle ear infections with the same accuracy as general practitioners and pediatricians.

‘The software-based system analyses images of the eardrum taken using an otoscope. The image is then uploaded to the cloud via a smartphone, where it's analyzed and compared with high-resolution images.’

The researchers at UmeƄ University have collaborated with the University of Pretoria in South Africa in their effort to develop an image-processing technique to classify otitis media. 




The software system consists of a cloud-based analysis of images of the eardrum taken using an otoscope, which is an instrument normally used in the medical examination of ears. Images of eardrums, taken with a digital otoscope connected to a smartphone, were compared to high-resolution images in an archive and automatically categorized according to predefined visual features associated with five diagnostic groups.
Tests showed that the automatically generated diagnoses based on images taken with a commercial video-otoscope had an accuracy of 80.6 percent while an accuracy of 78.7 percent was achieved for images captured on-site with a low-cost custom-made video-otoscope. This high accuracy can be compared with the 64-80 percent accuracy of general practitioners and pediatricians using traditional otoscopes for diagnosis.
"This method has great potential to ensure accurate diagnoses of ear infections in countries where such opportunities are not available at present. Since the method is both easy and cheap to use, it enables rapid and reliable diagnoses of a very common childhood illness," says Claude Laurent.
The method is described in the journal EBioMedicine.
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