Enhancing the Diagnosis of Sarcopenia Through Low-Dose Chest CT and Artificial Intelligence-Based Segmentation: Optimizing Resource Utilization in Healthcare
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 und...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
The Korean Society of Radiology
2025-07-01
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| Series: | Journal of the Korean Society of Radiology |
| Subjects: | |
| Online Access: | https://doi.org/10.3348/jksr.2023.0108 |
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| Summary: | 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/m2)
and skeletal muscle index (L1 SMI, cm2/m2) were measured on CT images using the software.
We compared the SMVI obtained through routine CT and LDCT in the same patients
and investigated the correlation between SMVI and L1 SMI.
Results The SMVI obtained through both routine CT and LDCT demonstrated statistically
significant associations with the conventional L1 SMI (r = 0.89, 0.85, p < 0.001). The SMVI obtained
through LDCT showed a slightly lower value but maintained a high correlation with
SMVI obtained through routine CT (r = 0.956, p < 0.001).
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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| ISSN: | 2951-0805 |