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Related Experiment Video

Updated: Sep 28, 2025

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Daniel Gourdeau1, Olivier Potvin2, Patrick Archambault2

  • 1Université Laval, Quebec, Canada. daniel.gourdeau.1@ulaval.ca.

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|April 5, 2022
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Summary

Deep learning models analyzing chest X-rays (CXRs) can predict COVID-19 patient outcomes. These models show promise in assessing disease severity and trajectory, potentially aiding clinical decisions.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Imaging Analysis
  • Pulmonology

Background:

  • Chest X-ray (CXR) findings are critical for managing COVID-19 patients and predicting disease outcomes.
  • Accurate assessment of current and future COVID-19 severity from radiological data is highly desirable for patient management.
  • Deep learning (DL) algorithms can extract complex features from medical images.

Purpose of the Study:

  • To develop and evaluate a DL-based approach for predicting COVID-19 severity and radiological trajectory using chest X-rays.
  • To assess the ability of DL-extracted features to stratify patient severity and predict disease progression ('Worse', 'Stable', 'Improved').

Main Methods:

  • A repurposed DL algorithm was trained on a large dataset (CheXnet) to extract radiological features.
  • Sequential CXRs from COVID-19 patients (open-source and local ICU datasets) were categorized based on radiological evolution.
  • Classical machine learning models were trained on DL-extracted features for severity evaluation and outcome prediction.

Main Results:

  • DL predictions significantly differentiated between 'Worse' and 'Improved' outcomes for specific radiological signs (Consolidation, Lung Lesion, Pleural effusion, Pneumonia; P < 0.05).
  • The model predicted the outcome category ('Worse' vs. 'Improved') with an Area Under the Curve (AUC) of 0.81 in an open dataset and 0.66 in an ICU dataset.
  • Disease severity was predicted with 52.3% accuracy, and the model demonstrated good generalization, classifying 81.6% of intubated ICU patients as critically ill.

Conclusions:

  • DL features extracted from CXRs show significant potential for classifying COVID-19 disease severity and predicting radiological trajectory.
  • The findings suggest that CXR-based DL analysis could inform clinical triage decisions.
  • Further validation with larger sample sizes and integrated clinical data is warranted.