Observational studyBMC pulmonary medicine2024
Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study.
Observational study in BMC pulmonary medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- Chest CT imaging toward personalized management of chronic obstructive pulmonary disease, asthma, and bronchiectasis: pulmonologist perspectives.Japanese journal of radiology · 2026Review
- Comorbidity of chronic obstructive pulmonary disease and pulmonary tuberculosis: a bibliometric analysis (2011-2025) with narrative review.Journal of thoracic disease · 2026Article
- Current diagnosis, epidemiology, and management of interstitial lung abnormalities.Frontiers in medicine · 2026Review
- A Comprehensive Review of Interstitial Lung Abnormalities.Respirology (Carlton, Vic.) · 2025Review
- Computed tomography morphological assessments of central airways in interstitial lung abnormalities and idiopathic pulmonary fibrosis.Respiratory research · 2024Article
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundInterstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts.
methodsILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans.
resultsILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure.
conclusionAI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.
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Registered trials
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