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    Deep learning applied to chest X-rays (CXRs) can identify individuals at high risk for chronic obstructive pulmonary disease (COPD). This AI tool shows additive predictive value beyond existing risk scores for incident COPD in both smokers and non-smokers.

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    Area of Science:

    • Artificial Intelligence in Medicine
    • Pulmonary Medicine
    • Radiology

    Background:

    • Chronic obstructive pulmonary disease (COPD) is a major global health concern.
    • Early identification of individuals at high risk for COPD is crucial for timely intervention.
    • Routine chest X-rays (CXRs) are widely available imaging modalities.

    Purpose of the Study:

    • To assess the efficacy of a deep learning model (CXR-Lung-Risk) in identifying individuals at high risk for incident COPD using routine outpatient CXRs.
    • To externally validate the CXR-Lung-Risk model's predictive capability for incident COPD.
    • To compare the model's performance against the established TargetCOPD clinical risk score.

    Main Methods:

    • External validation of a convolutional neural network (CXR-Lung-Risk) previously developed for lung-related mortality prediction.
    • Analysis of routine CXRs from outpatients without prior lung cancer, COPD, or emphysema.
    • Primary outcome: 6-year incident COPD. Discrimination assessed by AUC, stratified by smoking status.
    • Secondary analysis in Project Baseline Health Study (PBHS) for associations with pulmonary function and protein abundance.

    Main Results:

    • CXR-Lung-Risk demonstrated additive predictive value for 6-year incident COPD in both ever-smokers (AUC: 0.73) and never-smokers (AUC: 0.70) compared to the TargetCOPD score alone.
    • The model significantly improved prediction beyond known risk factors in both smoking groups (p<0.01).
    • Secondary analysis showed associations between CXR-Lung-Risk and poorer pulmonary function, as well as specific protein abundances (SCGB3A2, LYZ).

    Conclusions:

    • A deep learning model applied to routine CXRs can effectively identify individuals at high risk for developing COPD.
    • The CXR-Lung-Risk model offers significant added value over existing clinical risk scores for COPD prediction.
    • This AI-driven approach holds promise for improving early detection and management of COPD.