A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images

作者：Junbo Zeng, Weibiao Kang, Suijun Chen, Yi Lin, Wenting Deng, Yajing Wang, Guisheng Chen, Kai Ma, Fei Zhao, Yefeng Zheng, Maojin Liang, Linqi Zeng, Weijie Ye, Peng Li, Yubin Chen, Guoping Chen, Jinliang Gao, Minjian Wu, Yuejia Su, Yiqing Zheng, Yuexin Cai · 发表于：JAMA Otolaryngology–Head & Neck Surgery · 年份：2022 · DOI：10.1001/jamaoto.2022.0900 · 被引用次数：30 · 研究领域：Ear Surgery and Otitis Media、Hearing Loss and Rehabilitation、Hearing, Cochlea, Tinnitus, Genetics

Importance: Otitis media with effusion (OME) is one of the most common causes of acquired conductive hearing loss (CHL). Persistent hearing loss is associated with poor childhood speech and language development and other adverse consequence. However, to obtain accurate and reliable hearing thresholds largely requires a high degree of cooperation from the patients. Objective: To predict CHL from otoscopic images using deep learning (DL) techniques and a logistic regression model based on tympanic membrane features. Design, Setting, and Participants: A retrospective diagnostic/prognostic study was conducted using 2790 otoscopic images obtained from multiple centers between January 2015 and November 2020. Participants were aged between 4 and 89 years. Of 1239 participants, there were 209 ears from children and adolescents (aged 4-18 years [16.87%]), 804 ears from adults (aged 18-60 years [64.89%]), and 226 ears from older people (aged >60 years, [18.24%]). Overall, 679 ears (54.8%) were from men. The 2790 otoscopic images were randomly assigned into a training set (2232 [80%]), and validation set (558 [20%]). The DL model was developed to predict an average air-bone gap greater than 10 dB. A logistic regression model was also developed based on otoscopic features. Main Outcomes and Measures: The performance of the DL model in predicting CHL was measured using the area under the receiver operating curve (AUC), accuracy, and F1 score (a measure of the quality of a classifier, whic...