Deep Learning in Automated Region Proposal and Diagnosis of Chronic Otitis Media Based on Computed Tomography. Issue 3 (May 2020)

• Record Type: Journal Article
• Authors: Wang, Yan-Mei; Li, Yike; Cheng, Yu-Shu; He, Zi-Yu; Yang, Juan-Mei; Xu, Jiang-Hong; Chi, Zhang-Cai; Chi, Fang-Lu; Ren, Dong-Dong
• Is Part Of: Ear and hearing. Volume 41:Issue 3(2020)
• Publication Date: 2020-05
• Subjects: Artificial intelligence -- Cholesteatoma -- Deep learning -- Otitis media -- Tomography -- X-ray computed; Hearing disorders -- Periodicals; Audiology -- Periodicals; 612.85
• DOI: 10.1097/AUD.0000000000000794
• Languages: English
• ISSNs: 0196-0202

Abstract

Abstract : 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 cholesteatoma (76% versus 53%) cases, and superior consistency in duplicated cases (100% versus 81%) compared with clinical experts. Conclusions: This article presented a deep-learning framework that automatically extracted the region of interest from two-dimensional temporal bone CT slices and made diagnosis of COM. The performance of this model was comparable and, in some cases, superior to that of clinical experts. These results implied a promising prospect for clinical application of artificial intelligence in the diagnosis of COM based on CT images.