Deep Learning-Based Classification for Grading of Respiratory Distress Syndrome on Neonatal Chest Radiographs.

INTRODUCTION

The aim of this study was to develop and validate a deep learning-based multi-class classification model for automated grading of respiratory distress syndrome (RDS) severity on neonatal chest radiographs.

METHODS

A total of 23,210 radiographs, including normal and RDS cases, manually annotated by trained neonatologists, were divided into training, validation, and external test sets using patient-level splitting. Lung regions were segmented using UNet++, and RDS severity was classified into five ordered grades using a ResNet-50-based model.

RESULTS

The model achieved a quadratic weighted kappa of 0.696, with 85.7% of predictions within one grade and five-class accuracy of 0.575. AUROCs were 0.966 for detecting RDS and 0.866 for clinically significant RDS (grade ≥3). Gradient-weighted Class Activation Mapping demonstrated attention to lung regions with reduced aeration and granular opacities in severe RDS.

CONCLUSIONS

This model may provide objective and interpretable radiographic assessment of RDS severity and support more consistent radiographic evaluation in neonatal intensive care units.