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Updated: Jan 6, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Open-Source AI Model for Predicting Respiratory Mortality in COPD from Chest Radiographs.

Jong Hyuk Lee1, Chang-Hoon Lee2, Jayoun Kim3

  • 1Department of Radiology, Seoul National University Hospital, Seoul, Korea.

Radiology. Cardiothoracic Imaging
|October 30, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) scores from chest radiographs (CXR-Lung-Risk) effectively predict respiratory mortality in chronic obstructive pulmonary disease (COPD) patients. This AI tool offers superior prognostication compared to existing grading systems.

Keywords:
Artificial IntelligenceChest RadiographChronic Obstructive Pulmonary DiseasePrognostication

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Pulmonary Medicine and Respiratory Diseases

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) poses a significant global health burden.
  • Accurate prognostication is crucial for managing COPD patients and improving outcomes.
  • Existing risk stratification methods may not fully capture disease severity or progression.

Purpose of the Study:

  • To assess the clinical utility of artificial intelligence (AI) derived scores from chest radiographs (CXR-Lung-Risk) in predicting respiratory mortality.
  • To evaluate the performance of CXR-Lung-Risk scores against the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grading system.

Main Methods:

  • Retrospective analysis of 4226 COPD patients from a tertiary center (2011-2015).
  • CXR-Lung-Risk scores generated using an open-source AI algorithm applied to chest radiographs.
  • Multivariable Fine-Gray models used to assess the association between CXR-Lung-Risk and respiratory mortality, adjusting for clinical factors and lung function; discrimination evaluated using AUC.

Main Results:

  • CXR-Lung-Risk scores significantly predicted respiratory mortality (subdistribution hazard ratio per 5-year increase, 1.16; P < .001), demonstrating added value beyond lung function and clinical factors.
  • The area under the receiver operating characteristic curve (AUC) for CXR-Lung-Risk was 0.76, outperforming GOLD grades (AUC = 0.61; P < .001) in predicting mortality up to 10 years.
  • Higher CXR-Lung-Risk scores correlated with decreased pulmonary function (P < .001).

Conclusions:

  • CXR-Lung-Risk, an open-source AI tool, is a valuable and effective prognosticator for respiratory mortality in COPD patients.
  • This AI-driven approach offers improved risk stratification compared to traditional methods, potentially aiding clinical decision-making.
  • The findings support the integration of AI-powered image analysis into routine COPD management.