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

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Analysis shows AI enhances accuracy in diagnosing chronic otitis media via automated image quality assessment, indicating potential in primary care.

Automated classification achieves high accuracy through an AI diagnostic pipeline for otitis media.
Image quality assessment confirmed at 98.8% accuracy, supporting the reliability of the diagnostic process.
The CNN model's performance in classifying chronic otitis media showed significant promise for user training.
Integration into primary care settings may enable consistent diagnosis and educational resources for practitioners.

Seo et al. (2025) studied this question.

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Analysis shows AI enhances accuracy in diagnosing chronic otitis media via automated image quality assessment, indicating potential in primary care.