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Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma
BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
European Academy of Otology and Neurotology and the Politzer Society
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544284/ https://www.ncbi.nlm.nih.gov/pubmed/36999593 http://dx.doi.org/10.5152/iao.2023.221004 |
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author | Eroğlu, Orkun Eroğlu, Yeşim Yıldırım, Muhammed Karlıdag, Turgut Çınar, Ahmet Akyiğit, Abdulvahap Kaygusuz, İrfan Yıldırım, Hanefi Keleş, Erol Yalçın, Şinasi |
author_facet | Eroğlu, Orkun Eroğlu, Yeşim Yıldırım, Muhammed Karlıdag, Turgut Çınar, Ahmet Akyiğit, Abdulvahap Kaygusuz, İrfan Yıldırım, Hanefi Keleş, Erol Yalçın, Şinasi |
author_sort | Eroğlu, Orkun |
collection | PubMed |
description | BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis of chronic otitis media between January 2010 and January 2021 in our clinic were reviewed retrospectively. The patients were classified into the chronic otitis group without cholesteatoma (n = 34) and the chronic otitis group with cholesteatoma (n = 41) according to the presence of cholesteatoma at surgery. A dataset was created from the preoperative computed tomography images of the patients. In this dataset, the success rates of artificial intelligence in the diagnosis of cholesteatoma were determined by using the most frequently used artificial intelligence models in the literature. In addition, preoperative MRI were evaluated and the success rates were compared. RESULTS: Among the artificial intelligence architectures used in the paper, the lowest result was obtained in MobileNetV2 with an accuracy of 83.30%, while the highest result was obtained in DenseNet201 with an accuracy of 90.99%. In our paper, the specificity of preoperative magnetic resonance imaging in the diagnosis of cholesteatoma was 88.23% and the sensitivity was 87.80%. CONCLUSION: In this study, we showed that artificial intelligence can be used with similar reliability to magnetic resonance imaging in the diagnosis of cholesteatoma. This is the first study that, to our knowledge, compares magnetic resonance imaging with artificial intelligence models for the purpose of identifying preoperative cholesteatomas. |
format | Online Article Text |
id | pubmed-10544284 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | European Academy of Otology and Neurotology and the Politzer Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-105442842023-10-03 Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma Eroğlu, Orkun Eroğlu, Yeşim Yıldırım, Muhammed Karlıdag, Turgut Çınar, Ahmet Akyiğit, Abdulvahap Kaygusuz, İrfan Yıldırım, Hanefi Keleş, Erol Yalçın, Şinasi J Int Adv Otol Original Article BACKGROUND: In this study, we aimed to compare the success rates of computed tomography image-based artificial intelligence models and magnetic resonance imaging in the diagnosis of preoperative cholesteatoma. METHODS: The files of 75 patients who underwent tympanomastoid surgery with the diagnosis of chronic otitis media between January 2010 and January 2021 in our clinic were reviewed retrospectively. The patients were classified into the chronic otitis group without cholesteatoma (n = 34) and the chronic otitis group with cholesteatoma (n = 41) according to the presence of cholesteatoma at surgery. A dataset was created from the preoperative computed tomography images of the patients. In this dataset, the success rates of artificial intelligence in the diagnosis of cholesteatoma were determined by using the most frequently used artificial intelligence models in the literature. In addition, preoperative MRI were evaluated and the success rates were compared. RESULTS: Among the artificial intelligence architectures used in the paper, the lowest result was obtained in MobileNetV2 with an accuracy of 83.30%, while the highest result was obtained in DenseNet201 with an accuracy of 90.99%. In our paper, the specificity of preoperative magnetic resonance imaging in the diagnosis of cholesteatoma was 88.23% and the sensitivity was 87.80%. CONCLUSION: In this study, we showed that artificial intelligence can be used with similar reliability to magnetic resonance imaging in the diagnosis of cholesteatoma. This is the first study that, to our knowledge, compares magnetic resonance imaging with artificial intelligence models for the purpose of identifying preoperative cholesteatomas. European Academy of Otology and Neurotology and the Politzer Society 2023-07-01 /pmc/articles/PMC10544284/ /pubmed/36999593 http://dx.doi.org/10.5152/iao.2023.221004 Text en 2023 authors https://creativecommons.org/licenses/by-nc/4.0/ Content of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (https://creativecommons.org/licenses/by-nc/4.0/) |
spellingShingle | Original Article Eroğlu, Orkun Eroğlu, Yeşim Yıldırım, Muhammed Karlıdag, Turgut Çınar, Ahmet Akyiğit, Abdulvahap Kaygusuz, İrfan Yıldırım, Hanefi Keleş, Erol Yalçın, Şinasi Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma |
title | Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma |
title_full | Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma |
title_fullStr | Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma |
title_full_unstemmed | Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma |
title_short | Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma |
title_sort | comparison of computed tomography-based artificial intelligence modeling and magnetic resonance imaging in diagnosis of cholesteatoma |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544284/ https://www.ncbi.nlm.nih.gov/pubmed/36999593 http://dx.doi.org/10.5152/iao.2023.221004 |
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