Enhancing the Diagnosis of Sarcopenia Through Low-Dose Chest CT and Artificial Intelligence-Based Segmentation: Optimizing Resource Utilization in Healthcare - Hyeon Tak Lee; Jung Keun Hyun; Sang Yoon Kim (Journal of the Korean Society of Radiology)
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.