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Artificial intelligence to detect tympanic membrane perforations

Published online by Cambridge University Press:  02 April 2020

A-R Habib ,
E Wong ,
R Sacks  and
N Singh
A-R Habib*
Affiliation:
Department of Otolaryngology – Head and Neck Surgery, Westmead Hospital, Sydney, Australia Greenslopes Private Hospital, Ramsay Health Care, Brisbane, Australia
E Wong
Affiliation:
Department of Otolaryngology – Head and Neck Surgery, Westmead Hospital, Sydney, Australia
R Sacks
Affiliation:
Department of Otolaryngology – Head and Neck Surgery, Concord General Hospital, University of Sydney, Australia Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
N Singh
Affiliation:
Department of Otolaryngology – Head and Neck Surgery, Westmead Hospital, Sydney, Australia
*
Author for correspondence: Dr Al-Rahim Habib, Department of Otolaryngology – Head and Neck Surgery, Westmead Hospital, Sydney, Australia E-mail: [email protected]
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Abstract

Objective

To explore the feasibility of constructing a proof-of-concept artificial intelligence algorithm to detect tympanic membrane perforations, for future application in under-resourced rural settings.

Methods

A retrospective review was conducted of otoscopic images analysed using transfer learning with Google's Inception-V3 convolutional neural network architecture. The ‘gold standard’ ‘ground truth’ was defined by otolaryngologists. Perforation size was categorised as less than one-third (small), one-third to two-thirds (medium), or more than two-thirds (large) of the total tympanic membrane diameter.

Results

A total of 233 tympanic membrane images were used (183 for training, 50 for testing). The algorithm correctly identified intact and perforated tympanic membranes (overall accuracy = 76.0 per cent, 95 per cent confidence interval = 62.1–86.0 per cent); the area under the curve was 0.867 (95 per cent confidence interval = 0.771–0.963).

Conclusion

A proof-of-concept image-classification artificial intelligence algorithm can be used to detect tympanic membrane perforations and, with further development, may prove to be a valuable tool for ear disease screening. Future endeavours are warranted to develop a point-of-care tool for healthcare workers in areas distant from otolaryngology.

Keywords

OtoscopyEarTympanic MembraneMachine Learning
Type
Main Articles
Information
The Journal of Laryngology & Otology , Volume 134 , Issue 4 , April 2020 , pp. 311 - 315
Copyright
Copyright © JLO (1984) Limited, 2020

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Footnotes

Dr A-R Habib takes responsibility for the integrity of the content of the paper

Presented at the Australian Society of Otolaryngology – Head and Neck Annual Scientific Meeting, 22–24 March 2019, Brisbane, Australia.

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