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Hypothesis: The artificial intelligence and image processing technology can develop automatic diagnostic algorithm for pediatric otitis media (OM) with accuracy comparable to that from well-trained otologists. Background: OM is a public health issue that occurs commonly in pediatric population. Caring for OM may incur significant indirect cost that stems mainly from loss of school or working days seeking for medical consultation. It makes great sense for the homecare of OM. In this study, we aim to develop an automatic diagnostic algorithm for pediatric OM. Methods: A total of 1,230 otoscopic images were collected. Among them, 214 images diagnosed of acute otitis media (AOM) and otitis media with effusion (OME) are used as the database for image classification in this study. For the OM image classification system, the image database is randomly partitioned into the test and train subsets. Of each image in the train and test sets, the desired eardrum image region is first segmented, then multiple image features such as color, and shape are extracted. The multitask joint sparse representation-based classification to combine different features of the OM image is used for classification. Results: The multitask joint sparse representation algorithm was applied for the classification of the AOM and OME images. The approach is able to differentiate the OME from AOM images and achieves the classification accuracy as high as 91.41%. Conclusion: Our results demonstrated that this automatic diagnosis algorithm has acceptable accuracy to diagnose pediatric OM. The cost-effective algorithm can assist parents for early detection and continuous monitoring at home to decrease consequence of the disease. Key Words: Acute otitis media (AOM)—Automatic diagnosis—Joint sparse representation—Multitask learning—Object recognition—Otitis media with effusion (OME).
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