Deep Learning in Automated Region Proposal and Diagnosis of Chronic Otitis Media Based on Computed Tomography

作者：Yanmei Wang, Yike Li, Yu‐Shu Cheng, Ziyu He, Juanmei Yang, Jianghong Xu, Zhangcai Chi, Fanglu Chi, Dongdong Ren · 发表于：Ear and Hearing · 年份：2019 · DOI：10.1097/aud.0000000000000794 · 被引用次数：74 · 研究领域：Ear Surgery and Otitis Media、Sinusitis and nasal conditions、Tracheal and airway disorders

OBJECTIVES: The purpose of this study was to develop a deep-learning framework for the diagnosis of chronic otitis media (COM) based on temporal bone computed tomography (CT) scans. DESIGN: A total of 562 COM patients with 672 temporal bone CT scans of both ears were included. The final dataset consisted of 1147 ears, and each of them was assigned with a ground truth label from one of the 3 conditions: normal, chronic suppurative otitis media, and cholesteatoma. A random selection of 85% dataset (n = 975) was used for training and validation. The framework contained two deep-learning networks with distinct functions: a region proposal network for extracting regions of interest from 2-dimensional CT slices; and a classification network for diagnosis of COM based on the extracted regions. The performance of this framework was evaluated on the remaining 15% dataset (n = 172) and compared with that of 6 clinical experts who read the same CT images only. The panel included 2 otologists, 3 otolaryngologists, and 1 radiologist. RESULTS: The area under the receiver operating characteristic curve of the artificial intelligence model in classifying COM versus normal was 0.92, with sensitivity (83.3%) and specificity (91.4%) exceeding the averages of clinical experts (81.1% and 88.8%, respectively). In a 3-class classification task, this network had higher overall accuracy (76.7% versus 73.8%), higher recall rates in identifying chronic suppurative otitis media (75% versus 70%) and chol...