A retrospective evaluation of individual thigh muscle volume disparities based on hip fracture types in followed-up patientsopen access
- Authors
- Kim, Hyeon Su; Kim, Shinjune; Kim, Hyunbin; Song, Sang-Youn; Cha, Yonghan; Kim, Jung-Taek; Kim, Jin-Woo; Ha, Yong-Chan; Yoo, Jun-Il
- Issue Date
- Aug-2024
- Publisher
- PEERJ INC
- Keywords
- AI; Segmentation; CT; Radiology; Hip fracture; Thigh; Muscle volume; Disparity; UNETR
- Citation
- PEERJ, v.12, no.8
- Indexed
- SCIE
SCOPUS
- Journal Title
- PEERJ
- Volume
- 12
- Number
- 8
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/73629
- DOI
- 10.7717/peerj.17509
- ISSN
- 2167-8359
- Abstract
- Background: Hip fractures are a common and debilitating condition, particularly among older adults. Loss of muscle mass and strength is a common consequence of hip fractures, which further contribute to functional decline and increased disability. Assessing changes in individual thigh muscles volume in follow-up patients can provide valuable insights into the quantitative recovery process and guide rehabilitation interventions. However, accurately measuring anatomical individual thigh muscle volume can be challenging due to various, labor intensive and timeconsuming. Materials and Methods: This study aimed to evaluate differences in thigh muscle volume in followed-up hip fracture patients computed tomography (CT) scans using an AI based automatic muscle segmentation model. The study included a total of 18 patients at Gyeongsang National University, who had undergone surgical treatment for a hip fracture. We utilized the automatic segmentation algorithm which we have already developed using UNETR (U-net Transformer) architecture, performance dice score = 0.84, relative absolute volume difference 0.019 +/- 0.017%. Results: The results revealed intertrochanteric fractures result in more significant fi cant muscle volume loss (females: - 97.4 cm3, 3 , males: - 178.2 cm3) 3 ) compared to femoral neck fractures (females: - 83 cm3, 3 , males: - 147.2 cm3). 3 ). Additionally, the study uncovered substantial disparities in the susceptibility to volume loss among specific fi c thigh muscles, including the Vastus lateralis, Adductor longus and brevis, and Gluteus maximus, particularly in cases of intertrochanteric fractures. Conclusions: The use of an automatic muscle segmentation model based on deep learning algorithms enables efficient fi cient and accurate analysis of thigh muscle volume differences in followed up hip fracture patients. Our fi ndings emphasize the significant fi cant muscle loss tied to sarcopenia, a critical condition among the elderly. Intertrochanteric fractures resulted in greater muscle volume deformities, especially in key muscle groups, across both genders. Notably, while most muscles exhibited volume reduction following hip fractures, the sartorius, vastus and gluteus groups demonstrated more significant fi cant disparities in individuals who sustained intertrochanteric fractures. This non-invasive approach provides valuable insights into the extent of muscle atrophy following hip fracture and can inform targeted rehabilitation interventions.
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