An in-depth discussion of cholesteatoma, middle ear Inflammation, and langerhans cell histiocytosis of the temporal bone, based on diagnostic results - Duan, Bo; Pan, Li-Li; Chen, Wen-Xia; Qiao, Zhong-Wei; Xu, Zheng-Min (Frontiers in Pediatrics, 2022)
Objectives: This study aimed to conduct an in-depth investigation of the learning framework used for deriving diagnostic results of temporal bone diseases, including cholesteatoma and Langerhans cell histiocytosis (LCH). In addition, middle-ear inflammation (MEI) was diagnosed by computed tomography (CT) scanning of the temporal bone in pediatric patients. Design: A total of 119 patients were included in this retrospective study; among them, 40 had MEI, 38 had histology-proven cholesteatoma, and 41 had histology-proven LCH of the temporal bone. Each of the 119 patients was matched with one-third of the disease labels. The study included otologists and radiologists, and the reference criteria were histopathology results (70% of cases for training and 30% for validation). A multilayer perceptron artificial neural network (VGG16_BN) was employed and classified, based on radiometrics. This framework structure was compared and analyzed by clinical experts according to CT images and performance. Results: The deep learning framework results vs. a physician’s diagnosis, respectively, in multi-classification tasks were as follows. Receiver operating characteristic (ROC) (cholesteatoma): (0.98 vs. 0.91), LCH (0.99 vs. 0.98), MEI (0.99 vs. 0.85). Accuracy: (cholesteatoma): (0.99 vs. 0.89), LCH (0.99 vs. 0.97), MEI (0.99 vs. 0.89). Sensitivity: (cholesteatoma): (0.96 vs. 0.97), LCH (0.99 vs. 0.98), MEI (1 vs. 0.69). Specificity: (cholesteatoma) (1 vs. 0.89), LCH (0.99 vs. 0.97), MEI (0.99 vs. 0.89). Conclusions: This paper presents a research and learning framework for the diagnosis of cholesteatoma, MEI, and temporal bone LCH in children, based on CT scans. The research framework performed better than the clinical experts.