Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images

Ogawa, Masaki; Kisohara, Masaya; Yamamoto, Tasuku; Shibata, Shunsuke; Ojio, Yoshinao; Mochizuki, Kanako; Tatsuta, Ayame; Iwasaki, Shinichi; Shibamoto, Yuta

doi:10.1016/j.compbiomed.2022.105683

Cited by 14 publications

(2 citation statements)

References 13 publications

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“…In the otolaryngology field, a 3D VAE was used to detect inner ear abnormalities in CT images 21 . The model achieved a very high area under the ROC curve value of 0.99, and 99.1% sensitivity and 92.0% specificity, when using the colored pixel ratio to evaluate model performance.…”

Section: Discussionmentioning

confidence: 99%

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Chung

Choi

2023

Sci Rep

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We sought to establish an unsupervised algorithm with a three–dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved a sensitivity of 79.2%, specificity of 72.7%, accuracy of 77.1%, F1-score of 0.667, and AUROC of 0.801. Abnormal CT images correctly identified by the model showed differences in the reconstruction of extraocular muscles. The proposed model showed potential to detect abnormalities in extraocular muscles using a small dataset, similar to the diagnostic approach used by physicians. Unsupervised learning could serve as an alternative detection method for medical imaging studies in which annotation is difficult or impossible to perform.

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Section: Discussionmentioning

confidence: 99%

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Chung

Choi

2023

Sci Rep

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“…Recent studies have applied AI to otological imaging in various clinical contexts (Supplemental File 3, available online). These studies combined AI with otoscopy, [22][23][24][25][26]31,32,38,48,52,57,68,[74][75][76]79,81,84,85,93,95 computed tomography (CT), 30,36,41,42,50,62,63,70,71,88 and magnetic resonance imaging (MRI). 43,73,78 Most studies have focused on the image-based otoscopic diagnosis and automated segmentation of temporal bone CT for classifying normal and abnormal mastoid air cells.…”

Section: Application Of Ai In Otologymentioning

confidence: 99%

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Wang

Liang

et al. 2023

Otolaryngol.--head neck surg.

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ObjectiveTo update the literature and provide a systematic review of image‐based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.Data SourcesWeb of Science, Embase, PubMed, and Cochrane Library.Review MethodsStudies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.ResultsOverall, 686 studies were identified. After screening titles and abstracts, 325 full‐text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.ConclusionThis state‐of‐the‐art review aimed to highlight the increasing applications of image‐based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real‐world clinical practice. Future studies should consider 3‐dimensional (3D)‐based AI, such as 3D surgical AI.

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Improving Performance With Feature Selection, Extraction, and Learning

Patil,

Shinde,

Singh

et al. 2024

Integrating Metaheuristics in Computer Vision for Real‐World Optimization Problems

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Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images

Cited by 14 publications

References 13 publications

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder

Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review

Improving Performance With Feature Selection, Extraction, and Learning

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