PeerJ (Aug 2024)

A retrospective evaluation of individual thigh muscle volume disparities based on hip fracture types in followed-up patients: an AI-based segmentation approach using UNETR

  • Hyeon Su Kim,
  • Shinjune Kim,
  • Hyunbin Kim,
  • Sang-Youn Song,
  • Yonghan Cha,
  • Jung-Taek Kim,
  • Jin-Woo Kim,
  • Yong-Chan Ha,
  • Jun-Il Yoo

DOI
https://doi.org/10.7717/peerj.17509
Journal volume & issue
Vol. 12
p. e17509

Abstract

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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 time-consuming. 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 muscle volume loss (females: −97.4 cm3, males: –178.2 cm3) compared to femoral neck fractures (females: −83 cm3, males: −147.2 cm3). Additionally, the study uncovered substantial disparities in the susceptibility to volume loss among specific 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 and accurate analysis of thigh muscle volume differences in followed up hip fracture patients. Our findings emphasize the significant 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 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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