Development and Validation of a CNN-Based Diagnostic Pipeline for the Diagnosis of Otitis Media

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Analytical pipeline demonstrates high accuracy for diagnosing chronic otitis media, indicating potential for improving clinical diagnosis.

The diagnostic pipeline achieves high accuracy in chronic otitis media classification.
Utilizing deep learning models, the pipeline demonstrated a robust performance with an overall accuracy of 88.7%.
Image quality assessment plays a crucial role in enhancing diagnostic reliability across stages.
These advancements may enable better diagnosis in primary care settings and educational contexts for practitioners.

Seo et al. (2025) studied this question.

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Analytical pipeline demonstrates high accuracy for diagnosing chronic otitis media, indicating potential for improving clinical diagnosis.