Gender difference in cross-sectional area and fat infiltration of thigh muscles in the elderly population on MRI: an AI-based analysis

Background: Aging alters musculoskeletal structure and function, affecting muscle mass, composition, and strength, increasing the risk of falls and loss of independence in older adults. This study assessed cross-sectional area (CSA) and fat infiltration (FI) of six thigh muscles through a validated deep learning model. Gender differences and correlations between fat, muscle parameters, and age were also analyzed. Methods: We retrospectively analyzed 141 participants (67 females, 74 males) aged 52–82 years. Participants underwent magnetic resonance imaging (MRI) scans of the right thigh and dual-energy x-ray absorptiometry to determine appendicular skeletal muscle mass index (ASMMI) and body fat percentage (FAT%). A deep learning-based application was developed to automate the segmentation of six thigh muscle groups. Results: Deep learning model accuracy was evaluated using the “intersection over union” (IoU) metric, with average IoU values across muscle groups ranging from 0.84 to 0.99. Mean CSA was 10,766.9 mm² (females 8,892.6 mm², males 12,463.9 mm², p < 0.001). The mean FI value was 14.92% (females 17.42%, males 12.62%, p < 0.001). Males showed larger CSA and lower FI in all thigh muscles compared to females. Positive correlations were identified in females between the FI of posterior thigh muscle groups (biceps femoris, semimembranosus, and semitendinosus) and age (r or ρ = 0.35–0.48; p ≤ 0.004), while no significant correlations were observed between CSA, ASMMI, or FAT% and age. Conclusion: Deep learning accurately quantifies muscle CSA and FI, reducing analysis time and human error. Aging impacts on muscle composition and distribution and gender-specific assessments in older adults is needed. Relevance statement: Efficient deep learning-based MRI image segmentation to assess the composition of six thigh muscle groups in over 50 individuals revealed gender differences in thigh muscle CSA and FI. These findings have potential clinical applications in assessing muscle quality, decline, and frailty. Key Points: Deep learning model enhanced MRI segmentation, providing high assessment accuracy. Significant gender differences in cross-sectional area and fat infiltration across all thigh muscles were observed. In females, fat infiltration of the posterior thigh muscles was positively correlated with age.