Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections
Deep learning automatically detected retropharyngeal edema on MRI in acute neck infections. Areas under the receiver operating characteristic curve were 94.1% at the slice level and 94.8% at the patient level. The proposed convolutional neural network was lightweight and required only weakly annotated data.
Authors
- Oona Rainio (ORCID: https://orcid.org/0000-0002-7775-7656)
- Mikko J. Nyman (ORCID: https://orcid.org/0000-0002-2104-6302)
- Jussi Hirvonen (ORCID: https://orcid.org/0000-0002-0368-3746)
- Riku Klén (ORCID: https://orcid.org/0000-0002-0982-8360)
- Jaakko Heikkinen (ORCID: https://orcid.org/0000-0002-8007-5090)
- Janne Nurminen (ORCID: https://orcid.org/0000-0003-2482-9051)
- Heidi Huhtanen (ORCID: https://orcid.org/0000-0002-1581-1709)
- Jari-Pekka Vierula (ORCID: https://orcid.org/0009-0006-5933-5421)
Institutions
- University of Turku (FI)
- Turku University Hospital (FI)
- Turku PET Centre (FI)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-06-19
- DOI
- https://doi.org/10.1186/s41747-025-00599-6
- Citations
- 1
- Primary Topic
- Otolaryngology and Infectious Diseases
- Type
- article
- Field-Weighted Citation Impact
- 1.40
Funders
- Sigrid Juséliuksen Säätiö