ArticleJournal of the Korean Society of Radiology2023
CT-Derived Deep Learning-Based Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease.
Article in Journal of the Korean Society of Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 7 citations in OpenAlex.
- Independent validation of the Mosamatic deep learning automated skeletal muscle and adipose tissue segmentation tool in an external Chinese cancer patient cohort.BJR artificial intelligence · 2026Article
- Quantification of Skeletal Muscle at the First Lumbar Level for Prognosis in Amyotrophic Lateral Sclerosis.Journal of cachexia, sarcopenia and muscle · 2025Observational
- The association between skeletal muscle mass and all-cause mortality in acute exacerbation of chronic obstructive pulmonary disease.Frontiers in nutrition · 2025Article
- The development of an attention mechanism enhanced deep learning model and its application for body composition assessment with L3 CT images.Scientific reports · 2024Article
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods: A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRM Results: The volume and area of muscle and subcutaneous fat were negatively associated with PRM Conclusion: Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.
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Registered trials
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