Artificial Intelligence in Otitis Media Diagnosis: A Review of Diagnostic Accuracy, Limitations, Clinical Integration and Future Directions

Otitis media is a common pediatric ear infection that, if left undiagnosed or misdiagnosed can lead to complications such as hearing loss. Traditional diagnostic methods rely on subjective clinical assessments which can result in variability in accuracy. The integration of artificial intelligence, particularly deep learning offers a promising approach for automated and objective diagnosis. This study reviews the application of deep learning algorithms in the automatic detection of otitis media using otoscopic images, with a focus on deep metric learning techniques and their diagnostic performance. An evaluation of deep learning-based models including convolutional neural networks (CNNs) and deep metric learning, was conducted. Performance metrics such as sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC-ROC) were analyzed. A comparative evaluation of traditional CNN-based models and deep metric learning techniques was done to assess their relative strengths in diagnostic accuracy and generalization to diverse datasets. The advantages of deep metric learning in improving model generalization and robustness were also discussed. AI-driven models demonstrated high accuracy in detecting otitis media, with deep metric learning enhancing feature differentiation and classification performance. Several studies reported sensitivity and specificity values exceeding 90% with AUC-ROC values approaching 1.0, indicating strong diagnostic capability. However, variations in dataset quality, image preprocessing, and model interpretability remain key challenges. Additionally, this review explores the clinical feasibility of AI-based otoscopic analysis by assessing the integration of AI into routine otolaryngological practice. Deep learning, particularly deep metric learning holds significant potential for enhancing the automated diagnosis of otitis media in pediatric patients. Future research should focus on dataset standardization, model transparency, and real-world clinical validation to ensure widespread adoption in healthcare settings.