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  4. Analysing the feasibility of an automated AI-based classifier for detecting paranasal anomalies in the maxillary sinus

Analysing the feasibility of an automated AI-based classifier for detecting paranasal anomalies in the maxillary sinus

Publikationstyp
Journal Article
Date Issued
2023-05-12
Sprache
English
Author(s)
Hoffmann, Anna Sophie
Bhattacharya, Debayan
Medizintechnische und Intelligente Systeme E-1
Becker, Benjamin Tobias
Beyersdorff, Dirk
Petersen, Elina
Petersen, Marvin
Eggert, Dennis
Schlaefer, Alexander
Medizintechnische und Intelligente Systeme E-1
Betz, Christian Stephan
TORE-URI
https://hdl.handle.net/11420/45006
Journal
Laryngo-Rhino-Otologie
Volume
102
Issue
S 02
Start Page
200
Citation
Laryngo-Rhino-Otologie 102 (S 02): 200 (2023)
Publisher DOI
10.1055/s-0043-1767093
Publisher
Thieme
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
DDC Class
004: Computer Sciences
610: Medicine, Health
621: Applied Physics
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