Long-Term Follow-Up of Interstitial Lung Abnormalities in Low-Dose Chest CT in Health Screening: Exploring the Predictors of Clinically Significant Interstitial Lung Diseases Using Artificial Intelligence-Based Quantitative CT Analysis.

ABSTRACT:

Purpose

This study examined longitudinal changes in interstitial lung abnormalities (ILAs) and predictors of clinically significant interstitial lung diseases (ILDs) in a screening population with ILAs.

Materials and methods

We retrieved 36891 low-dose chest CT records from screenings between January 2003 and May 2021. After identifying 101 patients with ILAs, the clinical findings, spirometry results, and initial and follow-up CT findings, including visual and artificial intelligence-based quantitative analyses, were compared between patients diagnosed with ILD (n = 23, 23%) and those who were not (n = 78, 77%). Logistic regression analysis was used to identify significant parameters for the clinical diagnosis of ILD.

Results

Twenty-three patients (n

SUBMITTER: Jeong WJ 

PROVIDER: S-EPMC11625842 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature