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Machine learning for automated otoscopic tympanic membrane assessment

Year: 2025

Type: article

Abstract: Objectives: To develop and evaluate a deep learning model for automated classification of tympanic membrane and middle ear conditions using otoscopic images. Study Design: Retrospective. Materials and Methods: This study trained an EfficientNet-based convolutional neural network (CNN) on 618 labelled images across five categories: normal tympanic membrane (TM), acute otitis media (AOM), otitis media with effusion (OME), middle ear atelectasis (MEA), and TM perforation. The dataset was split into training (70%), validation (20%), and test (10%) sets. Model performance was assessed using accuracy, sensitivity, specificity, F1 score, and ROC-AUC. Results: On the test set, the model achieved an overall accuracy of 88.98%, sensitivity of 91.65%, specificity of 96.37%, F1 score of 0.89, and ROC-AUC of 0.975. Grad-CAM visualisations confirmed the model’s focus on clinically relevant areas. Conclusions: This study demonstrates that a CNN-based model can accurately classify common middle ear pathologies and has potential as a diagnostic support tool in telemedicine and primary care settings.

Source: DOAJ (DOAJ: Directory of Open Access Journals)

DOI: 10.34631/sporl.3125

Authors: Pires, Rafael, Pires, Rodrigo, Gasana, Sammy, Sousa Teles, Sofia, Couto, Ana Miguel, Oliveira, Leonor, Dentinho, Jorge, Antunes, Luís

Institutions: Hospital Garcia de Orta, Unidade Local de Saúde de Almada-Seixal, Portsmouth Hospitals NHS Trust, Carnegie Mellon University

Language: English

Topic: Ear Surgery and Otitis Media

Subfield: Otorhinolaryngology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7134116176

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