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

Abstract

Abstract Background The Global Initiative for Obstructive Lung Disease (GOLD) staging approach is frequently used to classify the severity of COPD by using spirometry. Recent advancements in artificial intelligence applications enable the automatic identification of COPD severity by chest computer tomography (CT). The goal of this study is to define the role of artificial intelligence in determining the severity of COPD. Methods We used a non-contrast CT chest and a computer-aided detection system (Coreline Soft's AVIEW), which was conducted as a descriptive cross sectional study and involved 80 cases. For the diagnosis of parenchymal disease using density mask methods such as inspiratory low attenuation area-950% (%LAA-950 HUINS) and D-value (cluster-size analysis), the 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 the severity of COPD. Results Based on the results of the spirometry, the patients were divided into four groups: mild (n = 23), moderate (n = 39), severe (n = 17), and very severe (n = 1). Insp. LAA-950 (%) in GOLD group 3 was substantially greater than in GOLD groups 2 and 1. Additionally, when compared to groups 2 and 1, the D-value in the GOLD 3 group was significantly higher. Conclusions Inspiratory LAA-950% and D-value were found to be significantly related to COPD severity as measured by dyspnea scale and spirometry. Inspiratory LAA-950% was effectively capable of distinguishing between patients with severe and moderate COPD.

Keywords

• Artificial intelligence
• Emphysema
• Inspiratory low attenuation area