Document Type
Article
Publication Date
10-10-2024
Abstract
Key points: While typically diagnosed with biopsy, ECRS may be predicted preoperatively with the use of AI. Various AI models have been used, with pooled sensitivity of 0.857 and specificity of 0.850. We found no statistically significant difference between the accuracy of various AI models.
Keywords
computer, diagnosis, eosinophils, machine learning, nasal polyps, neural networks, rhinosinusitis
Language
English
Publication Title
International Forum of Allergy and Rhinology
Rights
© 2024 The Author(s). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/), which permits non-commercial copying and redistribution of the material in any medium or format, provided the original work is not changed in any way and is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Rajan J, Rosen R, Karasik D, et al. A preliminary review of the utility of artificial intelligence to detect eosinophilic chronic rhinosinusitis. Int Forum Allergy Rhinol. 2025; 15: 203–207. https://doi.org/10.1002/alr.23463
Manuscript Version
Final Publisher Version