Abstract
Objective
Diagnosis of otosclerosis on temporal bone CT images is often difficult because the imaging findings are frequently subtle. Our aim was to assess the utility of deep learning analysis in diagnosing otosclerosis on temporal bone CT images.
Methods
A total of 198 temporal bone CT images were divided into the training set (n = 140) and the test set (n = 58). The final diagnosis (otosclerosis-positive or otosclerosis-negative) was determined by an experienced senior radiologist who carefully reviewed all 198 temporal bone CT images while correlating with clinical and intraoperative findings. In deep learning analysis, a rectangular target region that includes the area of the fissula ante fenestram was extracted and fed into the deep learning training sessions to create a diagnostic model. Transfer learning was used with the deep learning model architectures of AlexNet, VGGNet, GoogLeNet, and ResNet. The test data set was subsequently analyzed using these models and by another radiologist with 3 years of experience in neuroradiology following completion of a neuroradiology fellowship. The performance of the radiologist and the deep learning models was determined using the senior radiologist’s diagnosis as the gold standard.
Results
The diagnostic accuracies were 0.89, 0.72, 0.81, 0.86, and 0.86 for the subspecialty trained radiologist, AlexNet, VGGNet, GoogLeNet, and ResNet, respectively. The performances of VGGNet, GoogLeNet, and ResNet were not significantly different compared to the radiologist. In addition, GoogLeNet and ResNet demonstrated non-inferiority compared to the radiologist.
Conclusions
Deep learning technique may be a useful supportive tool in diagnosing otosclerosis on temporal bone CT.
Key Points
• Deep learning can be a helpful tool for the diagnosis of otosclerosis on temporal bone CT.
• Deep learning analyses with GoogLeNet and ResNet demonstrate non-inferiority when compared to the subspecialty trained radiologist.
• Deep learning may be particularly useful in medical institutions without experienced radiologists.
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Abbreviations
- AUC:
-
Area under the curve
- CI:
-
Confidence interval
- DICOM:
-
Digital Imaging and Communications in Medicine
- HU:
-
Hounsfield unit
- JPEG:
-
Joint Photographic Experts Group
- NPV:
-
Negative predictive value
- PPV:
-
Positive predictive value
- ROC:
-
Receiver operating characteristics
- ROI:
-
Regions of interest
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The scientific guarantor of this publication is Osamu Sakai.
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Written informed consent was waived by the Institutional Review Board.
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Study subjects or cohorts overlap
All study cohorts have not been previously reported in scientific papers. However, images of 5 patients out of 99 patients were presented in a review article: Andreu-Arasa VC, Sung EK, Fujita A, Saito N, Sakai O. Otosclerosis and Dysplasias of the Temporal Bone. Neuroimaging Clin N Am. 2019;29(1):29-47. doi:10.1016/j.nic.2018.09.004.
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Fujima, N., Andreu-Arasa, V.C., Onoue, K. et al. Utility of deep learning for the diagnosis of otosclerosis on temporal bone CT. Eur Radiol 31, 5206–5211 (2021). https://doi.org/10.1007/s00330-020-07568-0
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DOI: https://doi.org/10.1007/s00330-020-07568-0