| [188937] |
| Title: Analysing the feasibility of an automated AI-based classifier for detecting paranasal anomalies in the maxillary sinus. |
| Written by: S. A. Hoffmann and D. Bhattacharya and B. Becker and D. Beyersdorff and E. Petersen and M. Petersen and D. Eggert and A. Schläfer and C. Betz |
| in: (2023). |
| Volume: <strong>102</strong>. Number: (S 02), |
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| Publisher: Georg Thieme Verlag: |
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| DOI: 10.1055/s-0043-1767093 |
| URL: http://www.thieme-connect.com/products/ejournals/abstract/10.1055/s-0043-1767093 |
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Abstract: Large scale population studies have been performed to analyse the rate of finding sinus opacities in cranial MRIs. It is of interest whether there are findings requiring clarification. Using AI-based methods can automate the detection of the sinus opacities and reduce the workload of clinicians. In this work, a method for AI-based classification was developed in order to automatically recognise paranasal sinus opacities.