OBJECTIVE: In this study, wepropose an automatic diagnostic algorithm for detecting otitis media based on wideband tympanometry measurements. METHODS: We develop a convolutional neural network for classification of otitis media based on the analysis of the wideband tympanogram. Saliency maps are computed to gain insight into the decision process of the convolutional neural network. Finally, we attempt to distinguish between otitis media with effusion and acute otitis media, a clinical subclassification important for the choice of treatment. RESULTS: The approach shows high performance on the overall otitis media detection with an accuracy of 92.6%. However, the approach is not able to distinguish between specific types of otitis media. CONCLUSION: Out approach can detect otitis media with high accuracy and the wideband tympanogram holds more diagnostic information than the commonly used techniques wideband absorbance measurements and simple tympanograms. SIGNIFICANCE: This study shows how advanced deep learning methods enable automatic diagnosis of otitis media based on wideband tympanometry measurements, which could become a valuable diagnostic tool.

In this study, we propose an automatic diagnostic algorithm for detecting otitis media based on otoscopy images of the tympanic membrane. A total of 1336 images were assessed by a medical specialist into three diagnostic groups: acute otitis media, otitis media with effusion, and no effusion. To provide proper treatment and care and limit the use of unnecessary antibiotics, it is crucial to correctly detect tympanic membrane abnormalities, and to distinguish between acute otitis media and otitis media with effusion. The proposed approach for this classification task is based on deep metric learning, and this study compares the performance of different distance-based metric loss functions. Contrastive loss, triplet loss and multi-class N-pair loss are employed, and compared with the performance of standard cross-entropy and class-weighted cross-entropy classification networks. Triplet loss achieves high precision on a highly imbalanced data set, and the deep metric methods provide useful insight into the decision making of a neural network. The results are comparable to the best clinical experts and paves the way for more accurate and operator-independent diagnosis of otitis media.

Article Free Access Share on PIPADS—a low cost real-time visualization tool Authors: A. Spray Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, Australia Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, AustraliaView Profile , H. Schröder Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, Australia Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, AustraliaView Profile , K. T. Lie Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, Australia Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, AustraliaView Profile , E. Plesner Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, Australia Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, AustraliaView Profile , P. Bray Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, Australia Department of Electrical and Computer Engineering, The University of Newcastle, NSW 2308, AustraliaView Profile Authors Info & Claims Supercomputing '93: Proceedings of the 1993 ACM/IEEE conference on SupercomputingDecember 1993 Pages 398–407https://doi.org/10.1145/169627.169761Published:01 December 1993Publication History 1citation210DownloadsMetricsTotal Citations1Total Downloads210Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF