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

Te Yung Fang, Tse Yu Lin, Chung Min Shen, Su Yi Hsu, Shing Huey Lin, Yu Jung Kuo, Ming Hsu Chen, Tan Kuei Yin, Chih Hsien Liu, Men Tzung Lo, Pa Chun Wang

研究成果: 雜誌貢獻期刊論文同行評審


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.

頁(從 - 到)1590-1597
期刊Otolaryngology - Head and Neck Surgery (United States)
出版狀態已出版 - 6月 2024


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