Algorithm‐Driven Tele‐otoscope for Remote Care for Patients With Otitis Media

Objective The COVID‐19 pandemic has spurred a growing demand for telemedicine. Artificial intelligence and image processing systems with wireless transmission functionalities can facilitate remote care for otitis media (OM). Accordingly, this study developed and validated an algorithm‐driven tele‐ot...

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Bibliographic Details
Published in:	Otolaryngology-head and neck surgery Vol. 170; no. 6; pp. 1590 - 1597
Main Authors:	Fang, Te‐Yung, Lin, Tse‐Yu, Shen, Chung‐Min, Hsu, Su‐Yi, Lin, Shing‐Huey, Kuo, Yu‐Jung, Chen, Ming‐Hsu, Yin, Tan‐Kuei, Liu, Chih‐Hsien, Lo, Men‐Tzung, Wang, Pa‐Chun
Format:	Journal Article
Language:	English
Published:	England 01-06-2024
Subjects:	artificial intelligence automatic diagnostic algorithm otitis media otoscope telemedicine otoscope telemedicine otitis media artificial intelligence automatic diagnostic algorithm
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Summary:	Objective The COVID‐19 pandemic has spurred a growing demand for telemedicine. Artificial intelligence and image processing systems with wireless transmission functionalities can facilitate remote care for otitis media (OM). Accordingly, this study developed and validated an algorithm‐driven tele‐otoscope system equipped with Wi‐Fi transmission and a cloud‐based automatic OM diagnostic algorithm. Study Design Prospective, cross‐sectional, diagnostic study. Setting Tertiary Academic Medical Center. Methods We designed a tele‐otoscope (Otiscan, SyncVision Technology Corp) equipped with digital imaging and processing modules, Wi‐Fi transmission capabilities, and an automatic OM diagnostic algorithm. A total of 1137 otoscopic images, comprising 987 images of normal cases and 150 images of cases of acute OM and OM with effusion, were used as the dataset for image classification. Two convolutional neural network models, trained using our dataset, were used for raw image segmentation and OM classification. Results The tele‐otoscope delivered images with a resolution of 1280 × 720 pixels. Our tele‐otoscope effectively differentiated OM from normal images, achieving a classification accuracy rate of up to 94% (sensitivity, 80%; specificity, 96%). Conclusion Our study demonstrated that the developed tele‐otoscope has acceptable accuracy in diagnosing OM. This system can assist health care professionals in early detection and continuous remote monitoring, thus mitigating the consequences of OM.
Bibliography:	This article was presented at the AAO‐HNSF 2023 Annual Meeting & OTO Experience; September 30 to October 4, 2023; Nashville; Tennessee. These authors are joint first authors. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0194-5998 1097-6817
DOI:	10.1002/ohn.738