ArticleJournal of the Korean Society of Radiology2025
Enhancing the Diagnosis of Sarcopenia Through Low-Dose Chest CT and Artificial Intelligence-Based Segmentation: Optimizing Resource Utilization in Healthcare.
Article in Journal of the Korean Society of Radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Artificial Intelligence and Chest CT: A Smarter Path to Sarcopenia Detection.Journal of the Korean Society of Radiology · 2025Article
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3 authors.
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Abstract
Purpose: We aimed to apply artificial intelligence (AI)-based segmentation techniques to low-dose chest CT images to measure the muscle mass and investigate whether these results can be helpful in diagnosing sarcopenia. Materials and Methods: We retrospectively included 100 participants who had undergone both routine-dose contrast-enhanced chest CT (routine CT) and low-dose chest CT (LDCT) within 6 months of the study. Muscle segmentation and measurement were performed using a commercially available AI-based software. Skeletal muscle volume index (SMVI, g/m Results: The SMVI obtained through both routine CT and LDCT demonstrated statistically significant associations with the conventional L1 SMI ( Conclusion: Utilization of the LDCT protocol in diagnosing sarcopenia appears to be a valuable approach. By applying AI-based segmentation to these scans, it has become possible to accurately measure the entire muscle mass.
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