Toward Better Ear Disease Diagnosis: A Multi-Modal Multi-Fusion Model Using Endoscopic Images of the Tympanic Membrane and Pure-Tone Audiometry

• Published in: IEEE access Vol. 11; pp. 116721 - 116731
• Main Authors: Kim, Taewan, Kim, Sangyeop, Kim, Jaeyoung, Lee, Yeonjoon, Choi, June
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial intelligence; Artificial neural networks; Audiometry; Auditory system; Biomedical imaging; Bones; Classification; Classification algorithms; Computer aided diagnosis; Convolutional neural networks; Data models; Deep learning; Diagnosis; Diagnostic systems; Diseases; Ear; Ear diseases; Eardrum; Electronic medical records; Endoscopy; Hearing; Hearing loss; Machine learning; Media; Medical imaging; Membranes; Meningitis; Multilayer perceptrons; Multilayers
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3325346

Chronic otitis media is characterized by recurrent infections, leading to serious complications, such as meningitis, facial palsy, and skull base osteomyelitis. Therefore, active treatment based on early diagnosis is essential. This study developed a multi-modal multi-fusion (MMMF) model that automatically diagnoses ear diseases by applying endoscopic images of the tympanic membrane (TM) and pure-tone audiometry (PTA) data to a deep learning model. The primary aim of the proposed MMMF model is adding "normal with hearing loss" as a category, and improving the diagnostic accuracy of the conventional four ear diseases: normal, TM perforation, retraction, and cholesteatoma. To this end, the MMMF model was trained on 1,480 endoscopic images of the TM and PTA data to distinguish five ear disease states: normal, TM perforation, retraction, cholesteatoma, and normal (hearing loss). It employs a feature fusion strategy of cross-attention, concatenation, and gated multi-modal units in a multi-modal architecture encompassing a convolutional neural network (CNN) and multi-layer perceptron. We expanded the classification capability to include an additional category, normal (hearing loss), thereby enhancing the diagnostic performance of extant ear disease classification. The MMMF model demonstrated superior performance when implemented with EfficientNet-B7, achieving 92.9% accuracy and 90.9% recall, thereby outpacing the existing feature fusion methods. In addition, five-fold cross-validation experiments were conducted, in which the model consistently demonstrated robust performance when endoscopic images of the TM and PTA data were applied to the deep learning model across all datasets. The proposed MMMF model is the first to include a category of normal ear disease state with hearing loss. The developed model demonstrated superior performance compared to existing CNN models and feature fusion methods. Consequently, this study substantiates the utility of simultaneously applying PTA data and endoscopic images of the TM for the automated diagnosis of ear diseases in clinical settings and validates the usefulness of the multi-fusion method.