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Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT

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Abstract

Objective

Diagnosis of cochlear malformation on temporal bone CT images is often difficult. Our aim was to assess the utility of deep learning analysis in diagnosing cochlear malformation on temporal bone CT images.

Methods

A total of 654 images from 165 temporal bone CTs were divided into the training set (n = 534) and the testing set (n = 120). A target region that includes the area of the cochlear was extracted to create a diagnostic model. 4 models were used: ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The testing data set was subsequently analyzed using these models and by 4 doctors.

Results

The areas under the curve was 0.91, 0.94, 0.93, and 0.73 in ResNet10, ResNet50, SE-ResNet50, and DenseNet121. The accuracy of ResNet10, ResNet50, and SE-ResNet50 is better than chief physician.

Conclusions

Deep learning technique implied a promising prospect for clinical application of artificial intelligence in the diagnosis of cochlear malformation based on CT images.

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Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

We thank the members of our research groups for providing technical Assistance and participating in discussions.

Funding

This research was supported by Grants from Natural Science Foundation of Hunan Province (No. 2033JJ60302).

Author information

Authors and Affiliations

  1. Department of Otorhinolaryngology‐Head and Neck Surgery, Hunan Provincial People’s Hospital, The First Affiliated Hospital of Hunan Normal University, Changsha, Hunan, People’s Republic of China

    Zhenhua Li & Zhiqiang Tan

  2. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, People’s Republic of China

    Langtao Zhou

  3. Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China

    Xiang bin, Songhua Tan & Anzhou Tang

  4. Clinical Teaching Building, Guangxi Medical University, Nanning, 530000, People’s Republic of China

    Anzhou Tang

Authors
  1. Zhenhua Li
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  2. Langtao Zhou
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  3. Xiang bin
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  4. Songhua Tan
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  5. Zhiqiang Tan
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  6. Anzhou Tang
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Contributions

ZL wrote the main manuscript text; ST, BL, YX manually segmented important structures in the temporal bone; LZ performed the experiments; ZL, ST analyzed the data; AT designed research. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Anzhou Tang.

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Conflict of interest

The authors declare that there are no conflicts of interest regarding the publication of this article.

Ethical statement

This study was approved by The First Affiliated Hospital of Guangxi Medical University Medical Ethics Committee with Grant No. 2022-KY-E-(135).

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Cite this article

Li, Z., Zhou, L., bin, X. et al. Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT. Jpn J Radiol 42, 261–267 (2024). https://doi.org/10.1007/s11604-023-01494-z

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  • DOI: https://doi.org/10.1007/s11604-023-01494-z

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