What question did this study set out to answer?

To evaluate the effectiveness of deep learning models in diagnosing cochlear malformations using temporal bone CT images.

May 2, 2026

Deep learning for discriminating cochlear malformations on temporal bone CT.

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Randomized trial assesses deep learning for diagnosing cochlear malformations in temporal bone CT images, indicating diagnostic improvement.

To evaluate the effectiveness of deep learning models in diagnosing cochlear malformations using temporal bone CT images.
Utilized 373 temporal bone CT scans (187 normal, 186 malformed) for analysis.
Employed Swin UNETR for automatic cochlea segmentation and classified using ResNet50, EfficientNet-B0, and DenseNet121.
Analyzed a test set of 149 sides with deep learning models and otologist assessments.
Average Dice coefficient for Swin UNETR was 0.93.
AUCs for ResNet50, EfficientNet-B0, and DenseNet121 were 0.93, 0.89, and 0.93, respectively, with DenseNet121 performing best.
ResNet50 and DenseNet121 outperformed otologists in diagnosis.

Li et al. (2026) studied this question.

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May 2, 2026

View Full Paper

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Authors

Randomized trial assesses deep learning for diagnosing cochlear malformations in temporal bone CT images, indicating diagnostic improvement.

To evaluate the effectiveness of deep learning models in diagnosing cochlear malformations using temporal bone CT images.
Utilized 373 temporal bone CT scans (187 normal, 186 malformed) for analysis.
Employed Swin UNETR for automatic cochlea segmentation and classified using ResNet50, EfficientNet-B0, and DenseNet121.
Analyzed a test set of 149 sides with deep learning models and otologist assessments.
Average Dice coefficient for Swin UNETR was 0.93.
AUCs for ResNet50, EfficientNet-B0, and DenseNet121 were 0.93, 0.89, and 0.93, respectively, with DenseNet121 performing best.
ResNet50 and DenseNet121 outperformed otologists in diagnosis.

Li et al. (2026) studied this question.

View Full Paper

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