ArticleJapanese journal of radiology2024
Utility of deep learning for the diagnosis of cochlear malformation on temporal bone CT.
Article in Japanese journal of radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Etiology-Driven Personalized Cochlear Implantation: Implications for Electrode Choice, Timing, and Outcomes.Journal of personalized medicine · 2026Review
- Intelligent diagnosis of ossicular chain malformations on CT: development and clinical efficacy of a cascaded AI framework.Frontiers in bioengineering and biotechnology · 2026Article
- Image-based deep learning in otology: a state-of-the-art review.Frontiers in neurology · 2026Review
- CBCT radiomic features-based machine learning prediction models and nomogram for treatment decision-making regarding surgery-first approach in skeletal Class III malocclusion.BMC oral health · 2025Article
- Application of Artificial Intelligence in Otology: Past, Present, and Future.Journal of clinical medicine · 2024Article
- [Deep transfer learning radiomics model based on temporal bone CT for assisting in the diagnosis of inner ear malformations].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Article
- A Novel Time-Aware Deep Learning Model Predicting Myopia in Children and Adolescents.Ophthalmology scienceArticle
- Deep learning for discriminating cochlear malformations on temporal bone CT.Brazilian journal of otorhinolaryngologyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveDiagnosis 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.
methodsA 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.
resultsThe 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.
conclusionsDeep 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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