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

## TL;DR

This study shows that measuring retropharyngeal edema volume using MRI and deep learning can predict disease severity in acute neck infections better than traditional methods.

## Contribution

A deep learning model was developed for automated segmentation of retropharyngeal edema volume, which correlates better with clinical outcomes than binary presence/absence classification.

## Key findings

- Retropharyngeal edema volume correlates with ICU admission, CRP levels, abscess size, and hospital stay duration.
- The DL model achieved high accuracy in classifying RPE in sagittal slices and moderate segmentation performance.
- RPE volume is a better predictor of disease severity than binary RPE classification.

## Abstract

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.

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.

Patients who required ICU admission had significantly higher RPE volumes than those who did not, and RPE volume outperformed the binary RPE (presence/absence) in classification analysis of ICU admissions. Furthermore, RPE volume correlated positively with LOS, CRP, and maximal abscess diameter. At the slice level, the deep learning (DL)-based model achieved its highest area under the receiver operating characteristic curve (AUROC) in sagittal slices (98.2%) and its highest Dice similarity coefficient in axial slices (0.534).

RPE volume is a promising quantitative imaging biomarker associated with relevant clinical outcomes in acute neck infections. Our DL-based model enables automated quantification of RPE volume.

RPE volume provides clinically meaningful information in acute neck infections, outperforming binary classification in predicting disease severity and correlating with key clinical outcomes. Automated DL-based segmentation accurately locates the RPE and provides a moderate quantitative measurement of RPE volume, supporting its potential as a clinical imaging biomarker.

RPE volume correlated with markers of severe illness and outperformed binary RPE classification.We developed a DL-based algorithm for slice-wise classification and automatic segmentation of RPE.The classification model achieved excellent performance, while segmentation yielded modest Dice similarity coefficients consistent with prior imaging-based tumor segmentation algorithms.

RPE volume correlated with markers of severe illness and outperformed binary RPE classification.

We developed a DL-based algorithm for slice-wise classification and automatic segmentation of RPE.

The classification model achieved excellent performance, while segmentation yielded modest Dice similarity coefficients consistent with prior imaging-based tumor segmentation algorithms.