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Advancing Diagnostic Precision of Emphysema COPD
May 20, 2024
Machine-learning classification integrating non-invasive biomarkers, clinical characteristics and pulmonary function
Introduction
Computed tomography (CT) is used for evaluating phenotypic abnormalities in chronic obstructive
pulmonary disease (COPD), yet its cost and time-intensive nature limit routine use. Developing an
easily implementable technique for classifying emphysema extent is thus essential.
This study aimed to develop a diagnostic model to classify emphysema extent in COPD
patients relying solely on easily obtained measures such as clinical characteristics and
non-invasive biomarkers.
Poster
Conclusion
Diagnostic models incorporating easily obtainable measures effectively
distinguished COPD patients with high emphysema extent from those with
low extent. Such models for classifying emphysema patterns have the
potential for clinical implementation, aiding in diagnosis or serving as a
decision-making tool to determine the necessity of further CT scans.
