Abstract
Study Design: Retrospective cross-sectional study.Purpose: To investigate the relationship between sarcopenia-related markers and artificial intelligence (AI)–derived three-dimensional (3D) volumetric indices of lumbar muscles.Overview of Literature: Sarcopenia is characterized by loss of skeletal muscle mass and physical function. Although appendicular skeletal muscle mass (ASM) is widely used for sarcopenia assessment, reliable evaluation of lumbar paraspinal muscles remains limited. AIbased 3D volumetric analysis may provide a reproducible approach for muscle assessment.Methods: This single-center retrospective cross-sectional study included 58 women aged ≥65 years who underwent lumbar computed tomography and sarcopenia assessment. AI-derived 3D volumetric analysis was performed using TotalSegmentator implemented in 3D Slicer. Volumetric indices were calculated as muscle volume (cm3) divided by height3 (m3). Associations between volumetric indices and ASM measured by dual-energy X-ray absorptiometry were evaluated using Pearson correlation and multivariable linear regression analyses.Results: Among the 58 participants, 25 were classified as having sarcopenia. Appendicular skeletal muscle mass index (ASMI) was not significantly associated with paraspinal muscle volume indices. In contrast, the iliopsoas muscle volume index showed a significant positive correlation with ASMI (r=0.424, p<0.001). No significant differences in AI-derived 3D lumbar muscle volumetric indices were observed between sarcopenic and non-sarcopenic groups.Conclusions: Paraspinal muscle volume indices were not significantly associated with ASMI, whereas iliopsoas muscle volume indices showed a significant association. These findings suggest that the iliopsoas muscle may better reflect systemic skeletal muscle mass than paraspinal muscles in older women.