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Comparison of Deep Learning Approaches Using Chest Radiographs for Predicting Clinical Deterioration: Retrospective

Mahmudur Rahman¹, Jifan Gao², Kyle A Carey³

¹Department of Medicine, University of Wisconsin-Madison, 610 Walnut St, Madison, WI, 53792, United States, 1 608-262-9564.

|July 3, 2025

Summary

This summary is machine-generated.

Deep learning models using chest radiographs can predict clinical deterioration in hospitalized patients. The DenseNet121 model demonstrated superior performance in identifying patients at risk for intensive care unit transfer or death.

Keywords:

AI artificial intelligence chest chest X-ray chest radiographs clinical deterioration critical care data dataset deep learning deterioration hospitalized patient prediction predictive radiographs retrospective

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Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

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Published on: June 21, 2024

Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Clinical informatics

Background:

Early detection of clinical deterioration is crucial for improving patient outcomes.
Current early warning systems primarily use structured data, neglecting other predictive modalities.
Chest radiographs, often taken during deterioration, may offer valuable predictive information.

Purpose of the Study:

To compare and validate various computer vision models and data augmentation techniques for predicting clinical deterioration using chest radiographs.
To assess the efficacy of different deep learning architectures in identifying patients at high risk.

Main Methods:

Retrospective observational study of adult patients with elevated early warning scores (eCART).
Inclusion of patients with chest radiographs taken within 48 hours of the elevated score.
Comparison of five computer vision models (VGG16, DenseNet121, Vision Transformer, ResNet50, Inception V3) and four data augmentation methods.

Main Results:

The DenseNet121 model, pre-trained on chest radiographs with histogram normalization and Gaussian noise augmentation, achieved the highest predictive discrimination (AUROC 0.734, AUPRC 0.414).
The Vision Transformer model showed the lowest discrimination (AUROC 0.598).

Conclusions:

Chest radiographs hold significant potential for deep learning-based clinical deterioration prediction.
DenseNet121 demonstrated superior performance compared to other architectures.
Histogram normalization and random Gaussian noise augmentation may improve the performance of DenseNet121 and VGG16.