Assessment of artificial intelligence-aided chest computed tomography in diagnosis of chronic obstructive airway disease: an observational study

Maha M. Saad, Ahmed A. Bayoumy, Magdy M. EL-Nisr, Noha M. Zaki, Tarek H. Khalil & Ahmed F. ELSerafi
Egyptian Journal of Radiology and Nuclear Medicine
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This study assessed the role of artificial intelligence (AI) in determining the severity of chronic obstructive pulmonary disease (COPD) using chest CT. Conducted as a cross-sectional observational study involving 80 cases, the research utilized Coreline Soft's AVIEW software. Non-contrast chest CT scans were analyzed with AI to diagnose parenchymal disease through density mask methods like inspiratory low attenuation area-950% (%LAA-950 HUINS) and D-value (cluster-size analysis). Spirometry-based Tiffeneau index (TI), calculated as the ratio of forced expiratory volume in the first second (FEV1) to forced vital capacity, was used to assess COPD severity. Patients were classified into mild, moderate, severe, and very severe groups based on spirometry results. Findings showed that Inspiratory LAA-950% and D-value were significantly higher in severe COPD cases compared to moderate ones, indicating their effectiveness in distinguishing COPD severity. The study concludes that AI-aided CT analysis using AVIEW can effectively determine COPD severity.


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