Automated multi-class classification for prediction of tympanic membrane changes with deep learning models

Abstract

Backgrounds and objective Evaluating the tympanic membrane (TM) using an otoendoscope is the first and most important step in various clinical fields. Unfortunately, most lesions of TM have more than one diagnostic name. Therefore, we built a database of otoendoscopic images with multiple diseases and investigated the impact of concurrent diseases on the classification performance of deep learning networks. Study design This retrospective study investigated the impact of concurrent diseases in the tympanic membrane on diagnostic performance using multi-class classification. A customized architecture of EfficientNet-B4 was introduced to predict the primary class (otitis media with effusion (OME), chronic otitis media (COM), and ’None’ without OME and COM) and secondary classes (attic cholesteatoma, myringitis, otomycosis, and ventilating tube). Results Deep-learning classifications accurately predicted the primary class with dice similarity coefficient (DSC) of 95.19%, while misidentification between COM and OME rarely occurred. Among the secondary classes, the diagnosis of attic cholesteatoma and myringitis achieved a DSC of 88.37% and 88.28%, respectively. Although concurrent diseases hampered the prediction performance, there was only a 0.44% probability of inaccurately predicting two or more secondary classes (29/6,630). The inference time per image was 2.594 ms on average. Conclusion Deep-learning classification can be used to support clinical decision-making by accurately and reproducibly predicting tympanic membrane changes in real time, even in the presence of multiple concurrent diseases.

Feasibility of Multimodal Artificial Intelligence Using GPT-4 Vision for the Classification of Middle Ear Disease: Qualitative Study and Validation

“…The emergence of artificial intelligence (AI) has altered the landscape of medical technology, particularly in diagnosis, which leverages the identification of features based on imaging and physiological data [1][2][3]. In the field of otolaryngology, AI and deep learning models are being used for imaging; ongoing efforts focus on classifying diseases based on tympanic membrane images of middle ear disease [4][5][6]. Technological advancements, including deep learning and transfer learning using pre-trained models, have resulted in an accuracy range of 70-90% in models for analyzing otoscopic images [7].…”

Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence

“…In recent years, special otoscopes have been developed, such as multicolor imaging otoscopes, shortwave infrared otoscopes, and optical coherence tomography (OCT) in vivo. After screening relevant articles on the analysis of OM endoscopic images based on AI, a total of 43 relevant articles were identified ( Supplementary Table S1 ) [ 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. In most studies, the ML system achieved a high level of diagnosis and was as accurate as the clinician or even better.…”