Objectives: Otitis media is a common infection worldwide. Owing to the limited number of ear specialists and rapid development of telemedicine, several trials have been conducted to develop novel diagnostic strategies to improve the diagnostic accuracy and screening of patients with otologic diseases based on abnormal otoscopic findings. Although these strategies have demonstrated high diagnostic accuracy for the tympanic membrane (TM), the insufficient explainability of these techniques limits their deployment in clinical practice.
Methods: We used a deep convolutional neural network (CNN) model based on the segmentation of a normal TM into five substructures (malleus, umbo, cone of light, pars flaccida, and annulus) to identify abnormalities in otoscopic ear images. The mask R-CNN algorithm learned the labeled images. Subsequently, we evaluated the diagnostic performance of combinations of the five substructures using a three-layer fully connected neural network to determine whether ear disease was present.
Results: We obtained the receiver operating characteristic (ROC) curve of the optimal conditions for the presence or absence of eardrum diseases according to each substructure separately or combinations of substructures. The highest area under the curve (0.911) was found for a combination of the malleus, cone of light, and umbo, compared with the corresponding areas under the curve of 0.737-0.873 for each substructure. Thus, an algorithm using these five important normal anatomical structures could prove to be explainable and effective in screening abnormal TMs.
Conclusion: This automated algorithm can improve diagnostic accuracy by discriminating between normal and abnormal TMs and can facilitate appropriate and timely referral consultations to improve patients' quality of life in the context of primary care.
Keywords: Deep Learning; Mask R-CNN; Otitis Media; Otoendoscopy; Tympanic Membrane.