Acute deep neck infection MRI: deep learning segmentation and clinical relevance of retropharyngeal edema volume.

ABSTRACT:

Objective

Retropharyngeal edema (RPE) on MRI in patients with acute neck infection is associated with disease severity. We explored the potential role of RPE volume as a quantitative marker and developed a convolutional neural network (CNN) for automated RPE volume segmentation.

Materials and methods

Volumes of RPE were manually segmented from T2-weighted fat-suppressed Dixon magnetic resonance (MR) images from 244 patients. These volumes were correlated with clinical variables, such as the need for intensive care unit (ICU) admissions, C-reactive protein (CRP) levels, maximal abscess diameter, and length of hospital stay (LOS). Manually segmented masks were used to train a CNN.

Results

Patients who required ICU admission had significantly higher RPE volumes than thos

SUBMITTER: Viertonen VS 

PROVIDER: S-EPMC12929749 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature