Deep learning in ear imaging: a review of otoscopic classification and inner ear segmentation

Deep learning achieves favorable results for classifying otitis media via otoscopy and segmenting cochlea/ossicular chain on CT/MRI, but data scarcity for rare diseases and poor stapes segmentation limit clinical use.

• 2D convolutional neural networks (CNNs) are mature for otoscopic image classification, while 3D U-Net and UNETR architectures dominate CT and MRI analysis.
• Challenges include limited data for rare conditions, suboptimal segmentation of tiny structures like the stapes, and a need for better clinical workflow integration.

Automated summary

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