The Journal of International
Advanced Otology
Original Article

Comparison of Computed Tomography-Based Artificial Intelligence Modeling and Magnetic Resonance Imaging in Diagnosis of Cholesteatoma

1.

Department of Otorhinolaryngology, Fırat University, School of Medicine, Elazığ, Turkey

2.

Department of Radiology, Fırat University, School of Medicine, Elazığ, Turkey

3.

Department of Computer Engineering, Malatya Turgut Özal University, Faculty of Engineering and Natural Sciences, Malatya, Turkey

4.

Department of Computer Engineering, Fırat University, School of Engineering, Elazığ, Turkey

J Int Adv Otol 2023; 19: 342-349
DOI: 10.5152/iao.2023.221004
Read: 731 Downloads: 323 Published: 01 August 2023

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.

Cite this article as: Eroğlu O, Eroğlu Y, Yıldırım M, et al. Comparison of computed tomography-based artificial intelligence modeling and magnetic resonance imaging in diagnosis of cholesteatoma. J Int Adv Otol. 2023;19(4):342-349

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