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Deep learning techniques for ear diseases based on segmentations of normal tympanic membrane.

Background
Otitis media is a common infection affecting people worldwide. Owing to the limited number of ear specialists and rapid development in telemedicine, several trials have been conducted for developing novel diagnostic strategies to improve the diagnostic accuracy and screening of patients afflicted with otologic diseases based on abnormal otoscopic findings. Although these strategies have demonstrated a high diagnostic accuracy for the tympanic membrane (TM), the insufficient explainability of such techniques limits their deployment in clinical practices.


Methods
Herein, 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, the values were combined according to the five substructures using a three-layer fully connected neural network to determine whether an ear disease was present.


Results
We obtained the receiver operating characteristic (ROC) curves of the optimal condition for the presence or absence of eardrum diseases according to each substructure or a combination of substructures. The results indicated that the highest area under the curve ranged from a 0.911 true-positive rate in the ROC curve combined with malleus, cone of light, and umbo, compared with the corresponding range of 0.737-0.873 in 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 the diagnostic accuracy to discriminate between normal and abnormal TMs and facilitate appropriate and timely referral consultations to improve the quality of life in public healthcare.